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What makes an AI agent incident different?
An agent incident is a connected-system incident. The model may interpret instructions, call tools, use memory, and trigger actions in other services. Investigating the model’s text alone will not show the full impact: responders need to identify the agent’s identities, integrations, data access, authority, and downstream effects.
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Possible signals include prompt injection, tool misuse, data exfiltration, memory poisoning, unauthorized changes, cascading behavior across agents or workflows, cost abuse, and compromised dependencies. An anomalous answer or a successful prompt injection is a reason to investigate, not by itself a complete severity determination. Assess what the agent could reach and what it actually did.
OWASP’s LLM06:2025 guidance describes the danger of excessive agency: unexpected, ambiguous, or manipulated model output can lead to damaging actions when the system has excessive functionality, permissions, or autonomy. The practical distinction is that a model response may be the trigger, while the consequential action occurs through an identity, tool, or downstream service.
What should responders do first?
Follow the organization’s incident escalation path and establish control of the system before attempting a full root-cause analysis. The OWASP GenAI Incident Response Guide 1.0, published July 28, 2025, recommends AI-specific procedures, defined roles and decision points, familiarity with system architecture and logging, and exercises. It does not prescribe a universal hour-by-hour schedule; the sequence and pace below must be adapted to the deployment.
- Declare and assign ownership. Identify the agent, its owner, the affected business process, and the incident lead. Bring in the people who can control its runtime, identity, network access, and connected services.
- Establish whether harm is ongoing. Check whether the agent is still executing, whether queued or scheduled work remains, and whether other agents or workflows depend on the same identity, memory, tools, or artifacts.
- Choose an external containment point. Determine which orchestrator, runtime, identity provider, network layer, or downstream service can pause execution or deny access. Do not rely on a new prompt or policy instruction to the model as the containment boundary.
- Preserve essential evidence as containment proceeds. Capture volatile logs and session records where feasible, while prioritizing urgent revocation or shutdown if continued access presents unacceptable risk. Record what was disabled, when, by whom, and why.
How should you contain the agent and its authority?
Containment should be enforced outside the agent’s own decision process. Use the controls available in the actual architecture; there may not be one universal kill switch. Depending on the incident, stop or isolate execution, revoke the agent’s identity and tokens, narrow or disable risky tools, block relevant network paths, and deny access at downstream services. Revoke credentials that may be compromised and rotate secrets that interacted with the affected workflow if they may have been exposed.
Downstream systems should enforce authorization themselves rather than trusting model output to decide whether an action is allowed. OWASP’s excessive-agency guidance supports least privilege, approval for high-impact actions, monitoring, and rate limiting. If a tool can write, delete, transfer funds, administer accounts, or send information externally, assess that authority separately from read-only access.
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Containment can disrupt legitimate work or destroy useful evidence, so select its breadth based on the live risk. The following are decision aids, not an official severity matrix or a replacement for your organization’s criteria.
| Question | What to assess | Containment implication |
|---|---|---|
| Is harm still possible? | Is execution continuing, or has the risky activity stopped? Are queued jobs or scheduled actions still active? | Ongoing access or execution may require immediate suspension; a stopped event still needs scoping and credential review. |
| What scope shares the authority? | Is the issue confined to one session, or does it involve a shared identity, multi-agent workflow, or platform component? | Contain the narrowest boundary that reliably stops the activity; widen it if shared authority or dependencies create exposure. |
| What could the agent do? | Was access read-only, or could it write, delete, administer, spend, or act externally? | Higher-impact capabilities may justify disabling tools or downstream grants, not merely ending one session. |
| What may have propagated? | Did the run create artifacts, modify memory or configuration, or feed outputs to dependent consumers? | Quarantine or trace affected outputs and consumers where relevant; stopping the original run may not undo its effects. |
| What evidence is volatile? | Could logs, tokens, sessions, or queued work expire or change during containment? | Preserve what is feasible without delaying urgent access revocation or leaving dangerous authority active. |
What evidence and logs should you preserve?
Build a time-bounded activity record around the suspected run. Correlate by agent identity, human principal, session or workflow, tool invocation, accessed data, and downstream action wherever telemetry permits. Preserve relevant system architecture and logging details as well as records of actions taken during response.
- Model interaction: the prompts and other inputs received, model responses, and relevant context supplied to the run, including retrieved material where available.
- Execution trail: orchestrator and application audit records, tool calls and results, identity and access logs, network events, and downstream service records relevant to the activity.
- Effects: files, messages, transactions, configuration changes, generated artifacts, or other outputs produced by the run, together with evidence of consumers or dependencies where known.
- System context: relevant agent, tool, model, memory, integration, and logging configuration, plus the versions or components needed to interpret the records.
- Learning-related material: if training data or continual learning may be implicated, preserve the relevant data and a snapshot of the continuously learning model where applicable.
Protect evidence from routine expiry or alteration, restrict access to it, and document collection and custody according to your incident process. Do not copy exposed credentials into new logs or incident notes; record their identifiers or locations using approved secure handling. The evidence set depends on the architecture and incident type, and ordinary application logs may not capture model inputs, tool activity, memory changes, or downstream actions.
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How do you scope impact and decide what to disable?
Set the suspected time window, then trace outward from the affected identity and run. Look for earlier activity that could explain the behavior and later activity that could reflect its consequences. Where records permit, compare the agent’s intended scope with its actual permissions and observed actions.
- Identify all credentials, grants, service accounts, and human principals used by the workflow; determine which may need revocation or rotation.
- Check which tools and data sources were reachable, not only which ones appear in the visible conversation.
- Trace writes, deletes, administrative changes, external communications, or financial actions into the systems that would record them.
- Check whether outputs, altered memory, configuration, or artifacts were reused by other agents, pipelines, or people.
- Separate confirmed activity from possible exposure and unknowns, and preserve that distinction in incident updates.
These checks help decide whether to isolate a session, revoke a shared identity, disable a tool, or contain a broader platform or workflow. Avoid treating the absence of a model transcript entry as proof that no downstream action occurred; corroborate with the relevant service and audit records.
When can you eradicate the cause and restore service?
After containment and initial scoping, remove or correct the mechanism that enabled the incident. Depending on findings, that may mean removing malicious configuration or unauthorized persistence, replacing compromised credentials, disabling an affected extension, correcting permissions, or quarantining suspect artifacts. Determine whether downstream outputs need to be recalled, rebuilt, or otherwise handled before dependent workflows resume.
Restore access in stages only after checking that the relevant credentials, policies, integrations, and components are trustworthy. Confirm that authorization is enforced by downstream services, and monitor resumed activity for unexpected tool use or access. Keep a record of residual uncertainty and the controls used to manage it.
OWASP AISVS 1.0 Appendix C describes controls for AI-in-pipeline compromise, including credential revocation, secret rotation, artifact quarantine, evidence preservation, provenance tracing, and exercises for automated remediation. Those controls are especially relevant when an agent is part of a pipeline; they are not a complete universal playbook for every agent deployment. Where available, provenance records and AI bill-of-materials (AI BOM) information can help trace affected components and downstream artifacts.
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Use organization-specific escalation and notification procedures to involve security, system and business owners, legal counsel, privacy teams, and communications staff as warranted by the facts. Whether customers, regulators, or other parties must be notified—and by when—depends on jurisdiction, the data and services involved, and the incident circumstances. The cited OWASP guidance encourages preparation for legal and regulatory reporting scenarios, while AISVS says to notify regulators where applicable; neither establishes a universal deadline.
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Document key decisions, evidence gaps, containment actions, and recovery approvals. After the immediate response, update the system-specific runbook and rehearse the lessons in an exercise. NIST SP 800-61 Rev. 3, published in April 2025, places incident response within cybersecurity risk management under CSF 2.0 and supersedes Rev. 2. CISA and partners’ May 1, 2026 guidance on careful adoption of agentic AI services emphasizes limits on autonomy, strong identity, layered defenses, oversight, threat modeling, monitoring, and assessment; it is adoption guidance rather than a first-day response timeline.
A useful runbook names who can suspend each execution path, revoke each relevant identity, disable tools, preserve records, approve recovery, and make notification decisions. It should map those responsibilities to the actual agent architecture and be tested before an incident.
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