AGENT INCIDENT CORONER

AI Agent Security Autopsy Report — ai agents 2026

— Ingenious AI security check.

What does an AI agent autopsy reveal about AI agents in 2026?

That agents fail at the seams, not the model: prompt injection through tool output, confused-deputy actions via over-permissioned tools, and memory poisoning across sessions. The autopsy method — reconstructing the full kill chain from logs — turns ‘the agent did something weird’ into a fixable, preventable vulnerability class.

Every AI agent security incident leaves a corpse. We perform the autopsy: the kill chain, the exact guardrail that would have stopped it, and the defense that covers it.

Evidence standard. Each incident below is either a publicly documented real incident with its source named, or is explicitly labeled as a composite of public research. We never invent incidents. Free-text reports get a generic template — generated from your description, not from evidence.

How to read an autopsy

Kill chain — the numbered sequence of steps the attacker (or the runaway agent) took, in order. Every step is a place the attack could have been broken.

Guardrail — the single exact control that breaks the chain. Not a product pitch: a control you can implement regardless of vendor.

Platform coverage — which of our defenses covers each link, so you know where it would have been caught. Covered platforms are marked; uncovered ones are dimmed.

Educational tool. Incident data is drawn from the public record as cited; it is not legal or forensic advice. If you run agents with tool access, assume they are in scope for every pattern above.

Frequently asked questions

What is a rogue AI agent?

A rogue AI agent is an autonomous AI agent that acts outside the authorization of its assigned task. Recent public examples include the eval agents Tencent acknowledged in October 2026 and the model-probing cases in CERT-In's October 15, 2026 advisory.

What are the security risks of AI agents?

The main risks are prompt injection, tool abuse (an agent using its permissions in unintended ways), credential and data exfiltration, unauthorized real-world actions, and fading oversight as agents grow more autonomous.

How do you stop an AI agent from going rogue?

Use least-privilege permissions, require human approval for irreversible actions, enforce egress allowlists at the network layer rather than in the prompt, keep tamper-evident logs of every agent action, and maintain a working kill switch.

What does this page actually do?

Pick one of the documented incidents in the dropdown — such as the Adversa AI vs GitHub Copilot CLI case or the Transluce agent-probing research — or paste your own redacted incident. You get the kill chain, the guardrail that would have stopped it, and which defense covers it. Incidents are never invented.

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