Democratizing AI Security with Usable Knowledge and Intelligence
We build technical, code-level, no-BS AI defensive frameworks and tools that give security teams and developers the concrete countermeasures they need to reduce technical risk and protect AI systems from real-world threats.
Free and open-source. Not another governance checklist. AIDEFEND is a hands-on technical framework — every defensive technique ships with implementation guidance, architecture patterns, and ready-to-use code examples so your team can deploy real protections, not just policies.
Explore defenses by strategic function: Model, Harden, Detect, Isolate, Deceive, Evict, and Restore — each with code examples and implementation guidance.
Organize controls by stack component: Data, Model, Infrastructure, and Application layers with concrete architecture patterns.
Embed security across the AI lifecycle: Design, Build, Validate, Operate, Respond, and Restore.
Browse defenses cross-mapped to MITRE ATLAS, OWASP LLM/ML/Agentic AI, MAESTRO, NIST AML, and 9+ threat frameworks.
From prompt injection to autonomous agent swarms — a growing knowledge base of defensive techniques with implementation guidance and code examples for every major AI attack surface.
The #1 LLM threat. Attackers manipulate model behavior through crafted inputs that override system instructions.
Autonomous agents introduce new risks — unauthorized actions, goal drift, and privilege escalation across tool chains.
Model Context Protocol expands the attack surface. Tool poisoning, registry spoofing, and TOCTOU attacks threaten agent workflows.
Attackers inject malicious content into vector stores and knowledge bases, corrupting retrieval-augmented generation pipelines.
Compromised training data and untrusted model artifacts undermine AI integrity from the foundation up.
AI coding assistants can produce vulnerable or malicious code. Admission controls prevent unsafe code from reaching production.
When agents collaborate autonomously, rogue actors can infiltrate the swarm. Detect compromised agents before they cascade.
Turn the tables on attackers. Deploy decoy AI services, canary tasks, and honey data to detect and study adversaries in real time.
Persistent agent memory is a new attack surface. Poisoned memories can alter agent behavior long after the initial compromise.
Every defensive technique is explicitly mapped to known threats from the most critical AI security frameworks.
We believe that defending AI systems shouldn't be a privilege reserved for the largest organizations. Our mission is to democratize AI security defenses by developing and maintaining accessible frameworks, guidance, tools, and services that empower everyone to adopt AI safely and responsibly.
Freely accessible security intelligence and defensive guidance for the entire AI community.
Real-world countermeasures with implementation guidance, code examples, and tool recommendations.
Built for the community. Every contribution strengthens the collective defense of AI systems worldwide.
The AIDEFEND framework is just the beginning. We're actively building new tools, services, and capabilities to make AI security more actionable, automated, and accessible for teams of all sizes.
Stay connected — more announcements coming soon.