No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by sgInnora · Agent Tool · ★ 83
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is sentinel-reverse safe to install? View the security audit →
sentinel-reverse AI-Powered Autonomous Binary Reverse Engineering The native AI reverse engineering engine from Innora-Sentinel Zero API cost · Full data privacy · GPU-accelerated · Iterative analysis English · 中文 · 日本語 · 한국어 · Tiếng Việt · Español · Français · Deutsch · Português Why sentinel-reverse? Traditional reverse engineering tools (IDA Pro, Ghidra, radare2, Binary Ninja) require manual effort for every function — reading assembly, inferring logic, naming variables, and detecting vulnerabilities. For a binary with 500+ func
| Stars | 83 |
| Forks | 24 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 71.8082678071412/100 |
| Last Updated | 2026-02-05 |
| Created | 2026-02-05 |
| Platforms | cli, python |
| Est. Tokens | ~6k |
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sentinel-reverse is AI-Powered Autonomous Binary Reverse Engineering CLI — the native reverse engine from Innora-Sentinel. Local LLM inference (MLX), MPS GPU acceleration, multi-round iterative analysis, zero API cost.. It is categorized as a Agent Tool with 83 GitHub stars.
sentinel-reverse is primarily written in Python. It covers topics such as ai, apple-silicon, binary-analysis.
You can find installation instructions and usage details in the sentinel-reverse GitHub repository at github.com/sgInnora/sentinel-reverse. The project has 83 stars and 24 forks, indicating an active community.
sentinel-reverse is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to sentinel-reverse on Agent Skills Hub include ghidra-cli, ai_for_the_win, disrobe. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: