self-improvement-loops
Pass
Audited by Gen Agent Trust Hub on Jul 11, 2026
Risk Level: SAFE
Full Analysis
- [PROMPT_INJECTION]: The skill provides defensive guidance against prompt injection and reward hacking, specifically advising that security constraints (such as budgets and permissions) should be enforced at the runtime level rather than in mutable prompts, as agents in a self-improvement loop may "evolve away" prompt-based restrictions.
- [INDIRECT_PROMPT_INJECTION]: The skill acknowledges the attack surface created when agents process execution traces and failure patterns to drive self-modification. It recommends countermeasures such as explicitly declared editable surfaces, locked code regions, and deterministic merge logic for deltas.
- Ingestion points: Execution traces, score logs, and prior candidates stored within the experience archive (
SKILL.md). - Boundary markers: The skill explicitly recommends declaring editable surfaces (marked regions) while locking others and programmatically verifying immutability after each candidate.
- Capability inventory: The architecture describes coding agents capable of modifying harness source code (
harness.py), instruction prompts, and workflows. - Sanitization: Recommends using deterministic non-model logic for merging deltas and enforcing constraints at the runtime/OS level.
- [REMOTE_CODE_EXECUTION]: While the skill discusses systems that generate and run code (e.g., meta-agent search over harness code), it mandates strict sandboxing at the OS or container level to mitigate risks. The skill itself does not include any scripts that perform unauthorized remote code execution.
- [EXTERNAL_DOWNLOADS]: The skill includes informative references to reputable research papers and organizations (e.g., arXiv.org, METR, Anthropic, and known technical blogs like Lil'Log). These are standard academic citations and do not involve downloading or executing untrusted payloads.
Audit Metadata