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We build AI security for agentic systems by pairing context-aware automated red-teaming with vulnerability forecasting. We map how tools, permissions, and data interact in production, surface multi-step exploit chains, and distill those findings into runtime controls and release-blocking signals.
Autonomous agents invert traditional security assumptions and force new tradeoffs:
With faster deployment loops and multi-agent workflows, security teams are forced into triage. We focus on system-level risk and realistic exploit paths instead of isolated robustness checks.
We have trained an in-house context-aware attacker model that digests a customer's runtime environment, maps the full graph of tools and permissions, and generates multi-step exploits that target the seams between components. This approach has outperformed baseline red-teaming techniques by 60%+ on academic robustness benchmarks and powers our security evaluation platform. In parallel, we built a comprehensive RL environment to train red-teaming agents and distill successes and failures into a vulnerability forecasting model.
General Analysis applies a context-aware attacker model that maps tool and permission graphs, then generates multi-step exploit chains that expose system-level risks across interacting agents.
Our blue team distills red-team findings into vulnerability forecasting and runtime controls, enabling CI/CD security evaluation today and endpoint protection for AI agents next.
Our founding team brings together exceptional expertise from world-class institutions with backgrounds spanning quantitative trading, traditional security research, and AI research. Our team has published research in the top conferences (NeurIPS, ICLR, etc.) and has a combined $200K+ in ML competition winnings and $300K+ in security bug bounties.
Whether you're interested in our products, research, or joining our team, we'd love to hear from you.