How to Block Autonomous LLM Agents from Executing RCE
If you are building autonomous coding agents or integrating LLMs into your CI/CD pipeline, your greatest security threat is Agentic RCE (Remote Code Execution) via Prompt Injection.
Standard static analysis tools (like SonarQube or Snyk) struggle to detect this because the vulnerability is structural. The danger occurs when untrusted external I/O flows directly into an LLM context window, and the LLM's output flows into a dynamic execution command (like eval or os.system).
GitGalaxy solves this mathematically by mapping the physical distance between these nodes.
The GitGalaxy AI Guardrails
Instead of relying on rigid ASTs, GitGalaxy uses a specialized security_lens.py to hunt for the structural DNA of an RCE funnel.
1. Scan the Repository
Run the GitGalaxy engine against your agent framework:
2. Identify the Choke Points
GitGalaxy generates an _llm.md context brief and an _audit.json file. Look specifically for the Injection Surface Risk Exposure.
The engine calculates this by cross-multiplying the density of external network inputs against dangerous execution vectors, removing the "Agentic Shield" if the file is explicitly designed to handle LLM orchestration.
3. Deploy the Dev Agent Firewall
Once the high-risk files are identified, deploy the GitGalaxy Dev Agent Firewall (gitgalaxy/tools/ai_guardrails/dev_agent_firewall.py).
This script evaluates the token mass and blast radius of the target file. If a file is flagged as an RCE Funnel, the firewall restricts autonomous coding agents from modifying it, requiring a "Human in the Loop" (HITL) override.
Read the full technical specification for the AppSec Sensor: AI AppSec Sensor