Automate the process of verifying and patching security bugs found by an AI agent. This workflow uses a verifier sub-agent and a patching agent to propose and test fixes, with a human expert in the final review loop.
After an initial agent finds a vulnerability, pass its findings to a separate 'verifier' sub-agent. This agent's sole job is to confirm the exploit is legitimate and not a result of the first agent 'cheating' (e.g., by modifying the source code to introduce the bug).
Once a bug is fully verified, spin up a 'patching agent'. Its goal is to analyze the vulnerability and the surrounding code to generate a plausible code patch that resolves the issue.
Use the agentic system's build environment to automatically test the proposed fix. The system applies the patch, rebuilds the software, and runs the original HTML test case that triggered the crash. If the crash no longer occurs, the patch is considered successful.
The verified patch and a full report are passed to a human engineer for final review. The engineer provides essential oversight, checks for architectural implications, and makes the final decision to merge the fix, potentially expanding on the agent's localized solution.
Create an LLM-powered 'judge' to score and prioritize files in a large codebase for security analysis. This workflow helps focus expensive bug-hunting efforts on the most high-risk areas.
Create a custom AI agent that relentlessly hunts for security vulnerabilities in your codebase. This system uses a large language model with specific tools to find, test, and prove the existence of bugs.
Use a Codex Automation to create a 'meta-agent' that analyzes your codebase, identifies opportunities for new reusable agent skills, and then spawns sub-agents to automatically test and validate those new skills.
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