Broad, flexible access
Agents need broad, flexible access to tools, APIs, and data. That requirement clashes with least-privilege security models and pushes safety to the margins without a new paradigm.
About
We build the security layer for agentic systems.
The security gap
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.
Insufficient alignment
Governance-based approaches and general alignment training by labs are insufficient to make applications secure. Models are not aligned to a specific set of enterprise risks, so security must be active, adversarial, and continuous.
Nonlinear attack surfaces
AI systems have nonlinear and nondeterministic attack surfaces where interaction effects dominate. A component can look safe in isolation and still create exploit paths when combined with other agents, tools, and workflows.
What we've built
We train proprietary adversarial agents that digest a customer's runtime environment, map the full graph of tools and permissions, and generate multi-step exploits that target the seams between components. This approach greatly outperforms baseline red-teaming techniques on academic robustness benchmarks and powers our security evaluation platform. Our offensive capabilities allow us to forecast vulnerabilities during CI/CD and implement state-of-the-art defenses.
Our approach
Red Team
Automated Red-Teaming
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.
Blue Team
Strategic Blue-Teaming
Our blue team adversarially tests every security configuration you deploy and gives you the infrastructure to experiment until you land on the one that actually holds.
Team
Our founding team has published AI safety and security research in the world's most prestigious venues; worked on post-training at DeepMind, pretraining at Jane Street; and built the first safety models for policy enforcement at Cohere. More than half the team comes from Jane Street.
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