Detect prompt injections and jailbreak attempts in LLM prompts.
npx skills add https://github.com/OpenLAIR/dr-claw --skill prompt-guard-openlair
Please help me install this Agent Skill. Skill: prompt-guard Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/safety-alignment/prompt-guard Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill prompt-guard-openlair
Prompt-based systems are vulnerable to prompt injections and jailbreak attempts that can cause unsafe or undesired model behavior. This Skill provides a lightweight classifier to detect and block such inputs before they reach the model, reducing risk of data leakage and policy violations.
Provide a sample user prompt and observe the jailbreak score.
High-intent search queries and answers about installing and using this skill.
To detect prompt injections and jailbreak attempts in real-time, you can use an 86M parameter lightweight classifier that scores user inputs and API prompts before they reach the model, reducing risks of data leakage.
Yes, prompt injection detection offers plug-and-play integration with HuggingFace and RAG pipelines, allowing you to secure deployments by blocking malicious inputs before they trigger unsafe model behavior.
Multilingual prompt injection detection supports prompts across 8 languages, allowing you to classify diverse user inputs and identify jailbreak attempts regardless of the language used.
Using a fast 86M parameter classifier for real-time scoring is the best way to prevent data leakage from jailbreaks, as it evaluates API prompts quickly and blocks policy-violating inputs pre-inference.
While a lightweight classifier provides fast real-time scoring for prompt injections, it is designed as a pre-inference filtering layer and should be combined with broader safety mechanisms for complex adversarial edge cases.
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