self-improvement

Fail

Audited by Gen Agent Trust Hub on Oct 14, 2026

Risk Level: HIGHPROMPT_INJECTIONINDIRECT_PROMPT_INJECTIONDYNAMIC_EXECUTIONCOMMAND_EXECUTION
Full Analysis
  • [PROMPT_INJECTION]: The skill is explicitly designed to enable 'Recursive Self-Editing' and 'Evolutionary Prompts'. It instructs the agent to treat its system prompt and skill files as mutable 'DNA' that can be rewritten to change behavior. This provides a direct mechanism to bypass or replace initial safety guidelines and core instructions.
  • [DYNAMIC_EXECUTION]: The skill implements a process where the agent generates new instructions and integrates them into its environment using update_skill_file and replace_file_content. This is a form of self-modifying logic where the agent's behavior is dynamically altered at runtime based on locally generated (and potentially external-influenced) strings.
  • [COMMAND_EXECUTION]: The implementation protocol relies on shell-level operations such as git-committed and suggested tools like sed for file manipulation. Granting an agent the autonomy to execute file-system modifications on its own source code presents a significant risk of privilege abuse and logic corruption.
  • [INDIRECT_PROMPT_INJECTION]: The skill establishes a high-risk data processing pipeline where external input influences internal logic.
  • Ingestion points: The optimize_prompt function in SKILL.md accepts task_history (which includes external code execution results and user feedback) as the primary source for mutation analysis.
  • Boundary markers: The text recommends separating 'Prime Directives' into an immutable block, but the implementation logic does not provide a technical mechanism to enforce this separation, leaving safety blocks vulnerable to accidental or malicious overwrites.
  • Capability inventory: The skill uses replace_file_content and update_skill_file tools, alongside suggested git and shell commands, to persist modifications.
  • Sanitization: Although the security section states the agent 'MUST aggressively sanitize' failure patterns, the provided Python implementation logic lacks any concrete sanitization or validation steps before the LLM generates and integrates new instructions.
Recommendations
  • AI detected serious security threats
Audit Metadata
Risk Level
HIGH
Analyzed
Oct 14, 2026, 02:43 PM