Test AI applications for prompt injection vulnerabilities and analyze defenses.
npx skills add https://github.com/BishopCodes/OpenPAI --skill promptinjection
Please help me install this Agent Skill. Skill: PromptInjection Source: https://github.com/BishopCodes/OpenPAI/tree/main/skills/Security Command: npx skills add https://github.com/BishopCodes/OpenPAI --skill promptinjection
This Skill safeguards AI applications by proactively identifying and testing for prompt injection vulnerabilities, ensuring the AI adheres to its intended instructions and doesn't fall prey to malicious manipulation.
Use the PromptInjection skill to perform a full security assessment on the target application.
High-intent search queries and answers about installing and using this skill.
You can test AI applications for prompt injection by simulating direct and indirect injection, jailbreaking, and multi-stage attacks. This process involves mapping the attack surface, identifying injection points, and analyzing AI components to verify defenses against malicious manipulation.
Indirect prompt injection is a technique where malicious instructions are embedded in external data sources processed by the LLM. It compromises security by manipulating the AI into executing unauthorized commands or bypassing guardrails without direct user interaction.
Performing a security assessment to prevent chatbot jailbreaking requires reconnaissance and attack surface mapping to identify system prompt extraction vectors. Simulating multi-stage attacks tests the chatbot's adherence to intended instructions and verifies guardrail bypass defenses.
Yes, automated penetration testing can be used to extract system prompts from an LLM. By simulating targeted extraction techniques during the attack surface mapping phase, you can identify vulnerabilities that leak sensitive system information and analyze defense mechanisms.
The best way to map the attack surface of an AI application is through comprehensive reconnaissance that identifies injection points and analyzes LLM components. This approach ensures full security assessment coverage by highlighting areas susceptible to direct and indirect injection.
Multi-stage attacks bypass LLM guardrails by chaining together seemingly benign prompts that incrementally manipulate the AI's context. This technique exploits analytical weaknesses in the AI's instruction adherence, eventually forcing unauthorized command execution or system prompt extraction.
igbuendMap XSS attack surface by analyzing security headers, frameworks, and DOM patterns.
coval-aiAnalyzes adversarial reports to identify AI vulnerabilities and generate hardening strategies.
devkeniIdentify and remediate security vulnerabilities in LLM-based applications.
bpnrockstarTest LLM applications for prompt injection, jailbreak, and guardrail bypass vulnerabilities.
