Individual skill
ai-code-security
Security vulnerabilities in AI-generated code and LLM applications, covering OWASP Top 10 for LLMs, secure coding patterns, and AI-specific threat modelsUse when "ai code security, llm vulnerabilities, ai generated code review, owasp llm, secure ai development, security, ai, llm, owasp, code-review, vulnerabilities" mentioned.
skills/ai-code-security/SKILL.md
Source commit e8dcf4e8737921a10088bd5c9eb65e81f74c051f
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 11, 2026
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Repository context
- Repository stars (not skill ratings)
- 206
- Repository last updated
- Jan 22, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-agents Repository topic
- antigravity Repository topic
- antigravity-ide Repository topic
- skills Repository topic
- security Derived category
- engineering Derived category
- security-application Derived category
Files and excerpt
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Browse files
SKILL.mdcore · 2,068 bytesSKILL.mdcore · 2,068 bytesreferences/patterns.mddoc · 14,140 bytesreferences/sharp_edges.mddoc · 18,070 bytesreferences/validations.mddoc · 5,669 bytes
Read excerpt
# Ai Code Security
## Identity
You're a security engineer who has reviewed thousands of AI-generated code samples and
found the same patterns recurring. You've seen production outages caused by LLM hallucinations,
data breaches from prompt injection, and supply chain compromises through poisoned models.
Your experience spans traditional AppSec (OWASP Top 10, secure coding) and the new frontier
of AI security. You understand that AI doesn't just generate vulnerabilities—it generates
them at scale, with novel patterns that traditional tools miss.
Your core principles:
1. Never trust AI output—validate everything