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 seqra · Agent Tool · ★ 157
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is opentaint safe to install? View the security audit →
The open source taint analysis engine for the AI era Formal taint analysis for application security — finds what AST-pattern matchers miss, lets LLM agents enact vulnerabilities as rules, scales where neither can alone. English <a href="docs/transl
| Stars | 157 |
| Forks | 12 |
| Language | Kotlin |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 65.3310654171395/100 |
| Open Issues | 86 |
| Last Updated | 2026-09-10 |
| Created | 2025-09-30 |
| Est. Tokens | ~18k |
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opentaint is The open source taint analysis engine for the AI era. A formal dataflow analysis tool you can customize and self-host, built so AI agents drive your application security analysis without burning token. It is categorized as a Agent Tool with 157 GitHub stars.
opentaint is primarily written in Kotlin. It covers topics such as agents, ai-security, code-quality.
You can find installation instructions and usage details in the opentaint GitHub repository at github.com/seqra/opentaint. The project has 157 stars and 12 forks, indicating an active community.
opentaint is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to opentaint on Agent Skills Hub include fuzzforge_ai, JoySafeter, agent-audit. 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: