incident-response-smart-fix
Warn
Audited by Gen Agent Trust Hub on Aug 10, 2026
Risk Level: MEDIUMCOMMAND_EXECUTIONPROMPT_INJECTIONDATA_EXFILTRATION
Full Analysis
- [PROMPT_INJECTION]: The skill is susceptible to indirect prompt injection because it is designed to ingest and analyze untrusted external data such as error logs, distributed traces, and observability data ($ARGUMENTS).
- Ingestion points:
Phase 1: Issue AnalysisinSKILL.mdandresources/implementation-playbook.mdwhere the agent is instructed to analyze logs and observability data. - Boundary markers: The prompts in the implementation playbook do not specify any delimiters or boundary markers to separate the untrusted log data from the agent's instructions.
- Capability inventory: The skill workflow includes capabilities for shell command execution (
git bisect run) and routing tasks to other specialized agents. - Sanitization: There is no mention of sanitizing, escaping, or filtering the input data before it is processed by the LLM sub-agents.
- [COMMAND_EXECUTION]: The
resources/implementation-playbook.mdfile explicitly instructs the agent to perform automated root cause investigation using shell commands. - Evidence: The "Debugger Code Analysis" section provides a template for
git bisect run ./test_reproduction.sh. This command executes arbitrary shell scripts within the environment to identify the commit that introduced a bug. - [DATA_EXFILTRATION]: The skill's workflow requires the agent to access and analyze highly sensitive information.
- Evidence: The agent is prompted to analyze error traces, logs, and data from observability platforms like Sentry and DataDog, which often contain PII, API tokens, or other secrets.
Audit Metadata