This article discusses how context augmentation can significantly improve the effectiveness of Large Language Models (LLMs) like ChatGPT in bug bounty hunting. It emphasizes the use of external information to enhance recon, vulnerability chaining, and reporting, transforming LLMs into smart security assistants. #BugBounty #ContextAugmentation
Keypoints
- Context augmentation involves feeding external data into LLMs to make them task-specific.
- It improves recon analysis by clustering URLs and identifying dynamic endpoints or hidden parameters.
- JavaScript endpoint mining can be enhanced by extracting fetch/XHR calls and environment variable hints.
- Vulnerability chaining becomes more accurate when sanitized request/response data and API details are provided.
- AI-assisted reporting can generate detailed, context-aware bug reports, speeding up bug bounty workflows.
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