ai-llm-app-attack
AI/LLM应用攻击:提示注入,Agent工具滥用RCE,RAG投毒,MCP供应链,torch.load pickle RCE。Use when testing LLM apps, agents, RAG, MCP plugins, or AI model file risks.
68
82%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Critical
Do not install without reviewing
Security
2 findings: 1 critical severity, 1 medium severity. Installing this skill is not recommended: please review these findings carefully if you do intend to do so.
E006: Malicious code pattern detected in skill scripts.
What this means
Detected high-risk code patterns in the skill content — including its prompts, tool definitions, and resources — such as data exfiltration, backdoors, remote code execution, credential theft, system compromise, supply chain attacks, and obfuscation techniques.
Why it was flagged
This document explicitly describes and instructs on multiple exploitation techniques (prompt injection, SSRF, RCE, webshell writing, SQL exfiltration, model-pickle RCE), indicating clear malicious intent and actionable abuse patterns.
W013: Attempt to modify system services in skill instructions.
What this means
The skill prompts the agent to compromise the security or integrity of the user’s machine by modifying system-level services or configurations, such as obtaining elevated privileges, altering startup scripts, or changing system-wide settings.
Why it was flagged
Flagged because the prompt explicitly describes and encourages actions that read sensitive files (e.g., /etc/passwd), write webshells, perform SSRF to cloud metadata, and other tool-driven RCE/side-effect attacks that would modify or exfiltrate the host state.
- Repository
- Ed1s0nZ/CyberStrikeAI
- Commit
dad14c5
- Audited
- Security analysis
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.