overspace-labs/hae

HaE - Highlighter and Extractor, Empower ethical hacker for efficient operations. 赋能白帽,高效作战!

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Summary Information

Updated 44 minutes ago
Added to GitGenius on September 15th, 2026
Created on March 24th, 2020
Open Issues & Pull Requests: 0 (+0)
GitHub issues: Enabled
Number of forks: 318
Total Stargazers: 4,385 (+0)
Total Subscribers: 41 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.1 hours
Mean response time: 17.2 hours
90th percentile: 2.3 days
Tracked items: 62

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Issue Activity (beta)

Open issues: 0
New in 7 days: 0
Closed in 7 days: 0
Avg open age: N/A days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (27)

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Detailed Description

HaE is a Burp Suite extension that highlights and extracts data patterns to streamline security testing workflows.

The tool addresses the repetitive task of identifying and extracting sensitive information during penetration testing and bug hunting. It works by applying pattern-based highlighting and extraction rules within Burp Suite, allowing security researchers to quickly spot data of interest such as API keys, tokens, credentials, and other security-relevant patterns across intercepted traffic and responses. Rather than manually scanning through requests and responses, users define extraction rules that automatically flag matching content.

Security professionals conducting bug bounty work or internal penetration tests should consider this tool if they spend significant time manually reviewing traffic for sensitive data patterns. It suits workflows where consistent pattern recognition across many requests would otherwise be tedious. The extension integrates directly into Burp Suite, making it accessible to anyone already using that platform for security testing. Teams working on data security assessments or those needing efficient triage of findings will find the highlighting and extraction capabilities particularly valuable for reducing manual review time.

The project shows active maintenance with regular updates addressing user-reported issues and feature requests. Development includes responsiveness to community feedback, with fixes and improvements being incorporated based on user needs. The codebase receives ongoing refinement to improve reliability and expand pattern-matching capabilities. Contributors engage with reported problems and work to enhance the tool's effectiveness for its intended security testing use cases.