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AI/ML bug-bounty platform where researchers bypass LLM guardrails in timed challenges to win cash prizes.
Enterprise biometric platform for liveness, deepfake detection and face matching in KYC and fraud-prevention flows.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Privacy-focused CAPTCHA and bot/fraud-detection service, a drop-in reCAPTCHA alternative for websites and apps.
All-in-one digital-safety subscription protecting families from identity theft, fraud and online threats, with parental controls.
No public pricing
Free trial available
No public pricing
Free trial available
Free trial available
- ✦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
- ✦Active and passive liveness detection
- ✦Deepfake and AI-generated media detection
- ✦1:1 and 1:N face matching and search
- ✦Age estimation and document verification
- ✦iBeta Level 2 certified, low FAR/FRR
- ✦Lightweight mobile SDKs and REST APIs
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- ✦AI bot detection
- ✦Transaction fraud protection
- ✦Account-takeover (ATO) defense
- ✦Pull-based SMS MFA
- ✦Private Learning ML risk models
- ✦Two-line reCAPTCHA migration
- ✦Hundreds of integrations
- ✦Identity theft protection with insurance
- ✦3-bureau credit monitoring and lock
- ✦Antivirus, VPN and password manager
- ✦Online data removal from brokers
- ✦Parental controls and safe-gaming alerts
- ✦Dark-web and financial-fraud alerts
- →Red-teaming and jailbreaking LLMs
- →Earning bounties for AI exploits
- →Learning AI attack techniques
- →Competing against other researchers
- →Anti-spoofing liveness checks during KYC onboarding
- →Detecting deepfakes in live video calls
- →Step-up biometric authentication for risky actions
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects
- →Blocking bots and spam signups
- →Preventing account takeover
- →Reducing transaction and payment fraud
- →Stopping credential stuffing
- →Protecting against identity theft
- →Monitoring family credit and finances
- →Keeping kids safe online
- →Removing personal data from broker sites