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 detailed prompt that instructs the LLM to act as a security expert. Provide context on the different types of files in your codebase and your security bug classification criteria.
You're a security expert. Here's the different kinds of files we're looking at: C++ files, IPDL files, Web IDL files. Here is some detail about each... Now, give me two scores. One score is how likely do you think there's a memory safety issue? And another is how easy could you access this from a webpage?In the prompt, instruct the LLM to provide two scores for each file: 1) The likelihood that the file contains a specific type of vulnerability (e.g., memory safety issue). 2) How easily the code can be accessed by a malicious actor (e.g., from a webpage).
Automate the process of feeding each file (or relevant sections of files) to the LLM with the prompt. Collect the scores and compile a ranked list of files, sorted from most to least likely to contain a critical vulnerability.
Use the ranked list as the input for a more resource-intensive process, such as an automated bug-hunting agent. This ensures that your compute resources are focused on the highest-impact targets first.
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.
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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