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Open-source, Rust-built secure runtime that runs AI agents in encrypted enclaves so credentials never reach the model.
AI/ML bug-bounty platform where researchers bypass LLM guardrails in timed challenges to win cash prizes.
Automated AI red-teaming platform that discovers, tests, and defends AI models and agents against security threats.
Enterprise AI platform for redacting, anonymizing, and governing sensitive data across documents and AI workflows.
Cloud DLP and CASB that classifies and protects sensitive data across SaaS apps using deep learning (now Palo Alto Networks).
No public pricing
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Encrypted credential vault injected only at approved endpoints
- ✦Agents run inside Trusted Execution Environments (encrypted enclaves)
- ✦Sandboxed tools in Wasm containers with capability-based permissions
- ✦Real-time outbound leak detection to block credential exfiltration
- ✦Rust codebase for memory safety
- ✦One-click cloud deploy on NEAR AI Cloud or self-host from source
- ✦Timed AI-hacking challenges with cash pots
- ✦Public leaderboard and rankings
- ✦Guardrail-bypass and jailbreak objectives
- ✦Hacktivity feed of activity
- ✦Community via Discord
- ✦Blog on LLM exploits and AI security
- ✦Automated AI red teaming
- ✦AI discovery and attack-surface mapping
- ✦Agent-native reconnaissance
- ✦Exploitable-risk detection and reporting
- ✦Runtime AI protection and response
- ✦CI/CD, Burp Suite, and API integrations
- ✦AI-powered document redaction
- ✦Real-time data anonymization
- ✦AI guardrails for generative-AI apps
- ✦Automated compliance enforcement
- ✦Industry-specific solutions for government, legal, and healthcare
- ✦Deep-learning data classification (99.5% claimed accuracy)
- ✦Cloud DLP across SaaS applications
- ✦One-click deployment across apps, devices and users
- ✦End-user self-remediation of violations
- ✦Broad SaaS integrations
- ✦Insider-threat and breach monitoring
- →Running autonomous AI agents without exposing secrets
- →Self-hosting a secure personal AI assistant
- →Deploying agents in a confidential-compute environment
- →Red-teaming and jailbreaking LLMs
- →Earning bounties for AI exploits
- →Learning AI attack techniques
- →Competing against other researchers
- →Testing AI systems against evolving attacks
- →Finding vulnerabilities in models and agents
- →Securing agentic workflows in production
- →AI governance and compliance reporting
- →Automating FOIA and public-records redaction
- →Protecting privileged data in eDiscovery
- →Preventing data leakage to AI systems
- →Prevent data leaks across SaaS apps
- →Classify and monitor sensitive data
- →Reduce breaches from human error
- →Give security teams cloud data visibility