Incident response playbooks written before 2024 do not address vector store contamination, model weight integrity, prompt injection forensics, or agent audit log preservation. When an AI system is compromised, the investigation uses tools and procedures designed for traditional software. The evidence is different, the failure modes are different, and the containment steps are different.
Analysis Briefing
- Topic: Incident response gaps for AI system compromises and agentic deployments
- Analyst: Mike D (@MrComputerScience)
- Context: Originated from a live session with Claude Sonnet 4.6
- Source: Pithy Cyborg | Pithy Security
- Key Question: When your AI agent is compromised, what does your incident response plan tell you to do?
The Evidence That AI Incidents Produce and Where It Lives
A compromised traditional application produces evidence in system logs, network logs, and process execution records. Forensic investigation follows established chains from the initial access vector through lateral movement to the impact. The evidence artifacts are well-understood and the tools to collect them are mature.
A compromised AI agent produces different evidence in different places. The agent’s context window at the time of compromise is not persisted by default in most frameworks. The specific instructions the agent received through a tool output that triggered malicious behavior may be gone by the time the investigation starts. LangChain, LangGraph, and similar frameworks do not log full context windows unless you configure them to do so explicitly.
Vector store contamination leaves evidence in the index, but querying the index to find injected content requires knowing what to look for and having the tools to scan embedding vectors for suspicious content. Standard forensic toolkits do not include vector store forensics capabilities. The investigation has to start from suspicious outputs and work backward to the retrieval queries that produced them, which requires access to retrieval logs that many deployments do not generate.
Agent tool call logs are the most valuable forensic artifact in an AI system incident and among the least likely to be retained. Every tool call the agent made, with its arguments and the context that triggered it, is the equivalent of a process execution log in traditional forensics. Without it, reconstructing what the agent did and why is substantially harder.
The Containment Steps That Are Different for AI Systems
Traditional incident containment isolates affected systems, revokes compromised credentials, and patches the vulnerability. AI system containment requires additional steps that most IR teams have not practiced.
Vector store quarantine is the AI-specific equivalent of isolating a compromised database. If an attacker has injected malicious content into a RAG pipeline’s vector store, the injected content will continue to influence the agent’s behavior until it is identified and removed. Identifying injected content requires scanning the vector store for semantically anomalous entries and correlating them with the timeline of suspicious agent behavior.
Model weight integrity verification is relevant if the incident involves a self-hosted model. A compromised model serving infrastructure could be serving modified weights without detection unless a hash verification process exists. Comparing current weights against a known-good hash from the original download catches tampering. Most organizations running local models do not have this process in place.
Prompt injection forensics requires reconstructing the agent’s context at the time of the suspicious behavior. This requires context window logging enabled before the incident. Organizations deploying agentic systems without context window logging cannot fully reconstruct prompt injection attacks, which is the equivalent of deploying applications without any logging and then trying to investigate after a breach.
Building an AI-Aware IR Addendum
The goal is not to replace existing IR playbooks but to add an addendum that addresses the AI-specific steps alongside the standard ones.
The addendum needs four sections. First, preservation: what AI-specific artifacts to collect immediately (context window logs, tool call logs, vector store snapshots, model weight hashes). Second, analysis: how to scan vector stores for injected content, how to reconstruct agent behavior from tool call logs, how to identify the injection vector. Third, containment: how to quarantine the vector store, how to disable affected agent capabilities, how to verify model weight integrity. Fourth, recovery: how to restore a clean vector store from a pre-incident snapshot, how to verify the injection vector is closed before restoring service.
The AI agents hacking CI/CD pipelines scenario is exactly the class of incident that existing IR playbooks do not address because it combines traditional infrastructure compromise with AI-specific attack vectors that require AI-specific investigation steps.
What This Means For You
- Enable full context window logging for all production agent deployments before an incident occurs. Context window logs are the most valuable forensic artifact in an AI system investigation. They do not exist unless you configure them. Do it now.
- Take regular vector store snapshots and store them outside the production environment. A pre-incident snapshot allows you to diff the current index against the known-good state and identify injected content. Without a baseline, you are searching for anomalies with no reference point.
- Add AI system asset inventory to your IR runbook including which models are deployed, where weights are stored, which vector stores exist, and which agent frameworks are in use. Investigators who encounter an AI system incident without this inventory lose hours establishing basic context.
- Run a tabletop exercise specifically for an AI agent compromise scenario before you need to respond to a real one. The gaps in your current playbook will become obvious in a 90-minute tabletop, and closing them takes significantly less time than discovering them during an active incident.
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