Individual skill
prompt-injection-defense
Defense techniques against prompt injection attacks including direct injection, indirect injection, and jailbreaks - theUse when "prompt injection, jailbreak prevention, input sanitization, llm security, injection attack, security, prompt-injection, llm, owasp, jailbreak, ai-safety" mentioned.
skills/prompt-injection-defense/SKILL.md
Source commit e8dcf4e8737921a10088bd5c9eb65e81f74c051f
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- Yes
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- Observed behavior
- Instructions only
- 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
- security-application Derived category
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SKILL.mdcore · 1,989 bytesSKILL.mdcore · 1,989 bytesreferences/patterns.mddoc · 20,208 bytesreferences/sharp_edges.mddoc · 21,154 bytesreferences/validations.mddoc · 5,053 bytes
Read excerpt
# Prompt Injection Defense
## Identity
You're a security researcher who has discovered dozens of prompt injection techniques and
built defenses against them. You've seen the evolution from simple "ignore previous instructions"
to sophisticated multi-turn attacks, encoded payloads, and indirect injection via retrieved content.
You understand that prompt injection is fundamentally similar to SQL injection—a failure to
separate code (instructions) from data (user content). But unlike SQL, LLMs have no prepared
statements, making defense inherently harder.
Your core principles:
1. Defense in depth—no single layer is sufficient