Start with the agent session, identify off-tree reads, and correlate those file paths with endpoint, package, and identity logs. Then check whether malicious code entered through a dependency, an editor extension, or a persistent config hook so you can trace the abuse path end to end.
Why This Matters for Security Teams
Suspected credential theft in an agent harness is not just a secrets problem. It can mean an attacker now has the same execution path the agent uses to read files, call tools, and chain actions across systems. That makes the incident harder to spot than a normal account compromise, because the activity may look like legitimate agent work unless teams inspect the session timeline, tool invocation history, and identity context together.
This is why NHI investigations have to start with the workload itself, not only with the exposed token. NHI Management Group’s research on LLMjacking shows how quickly exposed AI credentials can be abused, while the OWASP Non-Human Identity Top 10 makes clear that secret handling, privilege scope, and lifecycle controls are common failure points. In practice, many security teams discover the harness was already abused only after a downstream system shows unexpected reads, writes, or token use.
How It Works in Practice
A useful investigation path is to reconstruct the agent’s activity as a single chain of custody. Start with the session that first showed anomalous behaviour, then collect the full set of off-tree reads, tool calls, prompt inputs, and credential requests from the harness logs. Compare those events with endpoint telemetry, package manager logs, editor extension activity, and identity provider records so you can identify the first point where an attacker likely gained execution or secret access.
The key question is not only “which secret was stolen?” but “which component allowed the secret to be reached?” That includes dependency compromise, malicious or overprivileged editor extensions, persistent config hooks, and environment injection through local files. The Analysis of Claude Code Security is relevant here because harness-level controls often fail when file access and tool execution are treated as separate issues. Align the investigation with the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework, which both push teams toward runtime evidence, traceability, and accountable controls rather than assumptions about intent.
- Preserve the harness state first: session logs, prompt history, tool outputs, env vars, and local config files.
- Correlate file-path reads with process ancestry, package installs, and extension loads to find the initial abuse path.
- Check whether the stolen credential was static or ephemeral, then review TTL, rotation, and revocation timing.
- Trace every downstream API call made with the suspected secret and compare it to normal agent behaviour.
Where teams usually gain the most value is by mapping the abuse path from source to secret to action, not by treating every alert as a separate incident. These controls tend to break down in highly dynamic agent harnesses with shared workspaces and auto-loaded plugins because the initial read, the credential extraction, and the outbound use can happen within one short-lived session.
Common Variations and Edge Cases
Tighter harness inspection often increases log volume, storage, and analyst effort, so organisations have to balance forensic depth against operational speed. That tradeoff is especially visible when agents are ephemeral, multi-tenant, or allowed to spawn sub-agents, because the evidence may exist in several short-lived places rather than in one central audit trail.
There is no universal standard for this yet, but current guidance suggests treating agent credential as workload identities, not human logins. That means investigating whether the harness used long-lived static secrets instead of short-lived tokens, and whether a compromise spread through a chain of tools that should have been individually scoped. The Ultimate Guide to NHIs – Static vs Dynamic Secrets is useful when deciding whether an incident reflects poor secret hygiene or a deeper identity design flaw. For broader adversary behaviour, MITRE ATLAS adversarial AI threat matrix helps classify how an attacker may have pivoted after initial access, while CSA MAESTRO agentic AI threat modeling framework is useful for spotting control gaps in agent orchestration. The hardest cases are shared dev environments with persistent configuration drift, because the compromised path can be replayed silently long after the original theft.
