Can you trust your autonomous agent?
Vijil provides tools for AI developers to test and improve the reliability, security, and safety of AI agents.
Reliability under Pressure
LLMs are unreliable because they fail to generalize beyond training data and they generate unverified predictions ("hallucinations").
Vulnerability to Attacks
LLMs are vulnerable to an unbounded set of attacks via jail breaks, prompt injections, data poisoning, and model tampering by malicious actors.
Propensity for Harms
LLMs have a propensity to generate toxic content, reinforce malicious stereotypes, produce unethical responses, and lead to unfair decisions.
Vijil red-team and blue-team cloud services harden models during fine-tuning, observe and defend agents and RAG applications during operation, and evaluate generative AI system reliability, security, and safety continuously.
Harden LLMs during development
Reduce vulnerability to attacks and mitigate technical risks before you deploy models
Defend LLMs during operation
Detect attacks and limit blast radius of models in production
Evaluate LLMs for Trust continuously
Test the system holistically under benign and hostile conditions to measure reliability, security, and safety with rigorous standards
Evaluate your AI agent in minutes
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