reverser_ai: Provides automated reverse engineering assistance through the use of local large language models on consumer

by mrphrazer · Agent Tool · ★ 1.2k

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How to install reverser_ai

git clone https://github.com/mrphrazer/reverser_ai.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 65/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars1,155
Forks93
LanguagePython
CategoryAgent Tool
LicenseGPL-2.0
Quality Score65/100
Last Updated2026-05-20
Created2024-03-17
Platformspython
Est. Tokens~15k

About reverser_ai

ReverserAI (v1.2) Author: Tim Blazytko Provides automated reverse engineering assistance through the use of local large language models (LLMs) on consumer hardware. Description: ReverserAI is a research project designed to automate and enhance reverse engineering tasks through the use of locally-hosted large language models (LLMs).

Compatible Skills

These tools work well together with reverser_ai for enhanced workflows:

  • kani — semantic(0.36)+complementary+same_lang+similar_pop+shared_platform (63%)
  • J-Wash — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (61%)
  • manim-generator — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (61%)
  • ablation — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (60%)
  • KiCAD-MCP-Server — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (60%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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