Practical, permission-first approaches to testing and strengthening large language models. Learn how to identify risks responsibly, follow disclosure best practices, and deploy safer systems.
We help teams discover, prioritize, and remediate AI model risks through ethics-first red teaming, threat modeling, and secure deployment guidance. All activities promoted here emphasize legal authorization, responsible disclosure, and harm minimization.
Prioritizing safety over exploitation — research for defense and resilience.
Perform testing only on systems you own or have explicit permission to test. Respect legal and contractual boundaries.
Design experiments to avoid releasing harmful outputs or creating enablement artifacts. Use simulated or sandboxed environments wherever possible.
Report discovered vulnerabilities through coordinated channels, provide remediation detail, and allow maintainers time to fix issues before public disclosure.
A practical checklist for secure LLM deployment: access controls, input sanitization, monitoring, and fallback behaviors.
OpenStructured templates to map attacker goals, assets, trust boundaries, and mitigations specific to conversational AI.
OpenSignals and alerts to catch anomalous inputs, policy bypass attempts, and potential misuse indicators without exposing exploit patterns.
OpenHands-on, ethics-centered training for engineering and security teams focused on detection, mitigation, and responsible testing methodologies.
Introductory module covering threat modeling, safe test design, and reporting workflows. Emphasis on non-destructive, permission-based exercises.
Request TrainingOperationalizing monitoring, policy enforcement, and fallback strategies for production LLMs to reduce risk surface.
Request TrainingFound a vulnerability or safety issue? We follow coordinated disclosure principles. Provide reproducible steps, impact assessment, and suggested mitigations. We do not publish exploit details that enable misuse.
We reserve the right to redact or withhold details that could enable abuse. Unauthorized scanning or testing of third-party systems is not permitted.
Use encrypted channels for sensitive data. Expect confidentiality during remediation.
High-level approaches to designing prompts that reduce risky outputs — without sharing exploit techniques.
ReadResearch summary on anomaly detection signals that indicate attempts to subvert model policies.
ReadA sanitized case study showing how coordinated testing helped a team improve model resilience.
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