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Automated AI red-teaming platform that discovers, tests, and defends AI models and agents against security threats.
Cloud DLP and CASB that classifies and protects sensitive data across SaaS apps using deep learning (now Palo Alto Networks).
AI/ML bug-bounty platform where researchers bypass LLM guardrails in timed challenges to win cash prizes.
Developer-first security platform unifying code, cloud, runtime and AI pentesting with noise reduction and autofix.
Open-source, Rust-built secure runtime that runs AI agents in encrypted enclaves so credentials never reach the model.
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No public pricing
No public pricing
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No public pricing
- ✦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
- ✦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
- ✦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
- ✦SAST, SCA and secrets scanning
- ✦Cloud misconfiguration (CSPM) and container scanning
- ✦AI-powered autonomous pentesting
- ✦AutoFix pull requests and auto-triage
- ✦Runtime and bot protection (Zen)
- ✦SOC 2 and ISO compliance support
- ✦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
- →Testing AI systems against evolving attacks
- →Finding vulnerabilities in models and agents
- →Securing agentic workflows in production
- →AI governance and compliance reporting
- →Prevent data leaks across SaaS apps
- →Classify and monitor sensitive data
- →Reduce breaches from human error
- →Give security teams cloud data visibility
- →Red-teaming and jailbreaking LLMs
- →Earning bounties for AI exploits
- →Learning AI attack techniques
- →Competing against other researchers
- →Finding and fixing code vulnerabilities
- →Securing cloud and containers
- →Running continuous pentests
- →Automating compliance evidence
- →Running autonomous AI agents without exposing secrets
- →Self-hosting a secure personal AI assistant
- →Deploying agents in a confidential-compute environment