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Cyber Security Guy (@HaroonWMalik) on X
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Cyber Security Guy (@HaroonWMalik) on X

Thread by Haroon Malik adding Stages 13 to 17 to an AI Security Engineering roadmap, covering runtime security. Stages: runtime detection and AI telemetry, AI supply chain (unsafe model formats, model signing, AI-BOMs, slopsquatting, malicious MCP servers), agent containment with egress control, shadow AI inventory, and AI incident response. Each stage lists topics to learn, a lab to practice and a rationale. It also amends Stage 11: agent self-written audit logs need independent verification. The captured text cuts off at Stage 1.

TL;DR Extends an AI security learning roadmap with five runtime stages: telemetry, supply chain, agent containment, shadow AI inventory and incident response.
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What it is

A thread by Haroon Malik (@HaroonWMalik) that adds Stages 13 to 17 to an AI Security Engineering roadmap, covering what happens after deployment. The thread's premise is that recent agentic intrusions "failed at runtime", not at the prompt. The captured text stops at the start of Stage 1 (OWASP Top 10 for LLMs, STRIDE for AI, the confused deputy problem), so the earlier stages are not visible.

What each stage asks you to build

  • Stage 13, runtime detection: Instrument an agent, stream its telemetry to a SIEM, and write detections for unusual tool use and new destinations. Learn OpenTelemetry for agents, tool-call and MCP logging, AI gateway logs and behavioural baselining. The stated reason is that agent logs are self-reported, so independent evidence of what the agent did is needed.
  • Stage 14, supply chain: Build a CI gate that scans model artifacts for unsafe formats (pickle, HDF5) and verifies signatures before deployment. Topics also include AI-BOMs, slopsquatting (hallucinated package names) and malicious MCP servers. The thread calls a poisoned model file "RCE with a friendly name".
  • Stage 15, infrastructure and containment: Lock down a code-executing agent so it reaches only approved destinations, then try to break out. Topics include exposed model servers and AI workflow tools, LLMjacking (stolen API keys), GPU hijacking and egress control for sandboxes.
  • Stage 16, shadow AI: Build a live inventory of every model, agent and MCP server in a lab network, with what each talks to. Discovery draws on network, endpoint and cloud data, and covers non-human identity sprawl.
  • Stage 17, incident response: Run a tabletop where an agent goes rogue at 3am, and settle who kills it, how, and what evidence is kept. Topics include machine-speed containment, credential revocation and agent session forensics.

Amendment to Stage 11

An audit log the agent writes about itself proves nothing. Verify it against an independent source. Stage 13 repeats this point.

Caveats

The thread names topics and labs but gives no tools, commands, detection queries or incident references. It says "in recent incidents the signals fired" and nobody connected them, without naming any incident. Use it as a curriculum checklist, not a procedure.

Organisations named here

  • Haroon Malik

Technologies named here

  • OpenTelemetry
  • Model Context Protocol
  • SIEM
  • LLM

Standards and laws named here

  • OWASP Top 10 for LLM Applications

Techniques named here

  • Slopsquatting
  • LLMjacking
  • Sandbox escape
  • STRIDE
  • Agent session forensics
  • Behavioural baselining
  • Credential revocation
  • Egress control
  • GPU hijacking
  • Model signing
  • STRIDE for AI
  • Tool-call and MCP logging

Concepts named here

  • Shadow AI
  • AI-BOM
  • confused deputy
  • Confused Deputy problem
  • Independent evidence of agent behaviour
  • Malicious MCP servers
  • Non-human identity sprawl
  • Runtime security
  • Unsafe model formats (pickle, HDF5)