General Analysis Raises $10M in Seed Funding Led by Altos Ventures

The company plans to use the funds to grow its operations and continue developing its products.

General Analysis is building security infrastructure for agentic AI to address growing security risks as companies deploy autonomous AI agents in production. The company already works with enterprise customers in support and finance, serving products and workflows used by hundreds of millions of users.

The founders built the company on the idea that securing AI agents is a different kind of problem from traditional cybersecurity. AI agents act in unpredictable ways, and their failures can’t be fully understood just by reviewing code.

In March, General Analysis tested its adversarial AI agent against 55 live customer service bots. It successfully tricked 50 of them into giving away fake rewards worth over $10 million each in just a few minutes, while only five resisted. The company uses this kind of testing to identify weaknesses and improve defenses before AI agents are deployed in real business environments.

Last summer, General Analysis researchers found a security flaw in a Supabase integration used by Cursor. The issue showed that a single malicious support ticket could potentially trick an internal AI agent into exposing a private database. British engineer Simon Willison, who coined the term “prompt injection,” described this as a “lethal trifecta” situation where an AI system handles private data, processes untrusted inputs, and can send information externally.

General Analysis helps companies improve AI security by testing systems in real-world scenarios. It uses adversarial testing and defensive tools to identify weaknesses in AI agents, assess how different protections perform, and demonstrate which configurations reduce risk. The company’s approach is based on the idea that there is no single security setup that works for all AI systems, so each deployment should be evaluated, and tuned to its own risks.

Read More:Counterpart Raises $50M in Series C Funding Led by Valor Equity Partners

“One advantage of agents is that they are much easier to study systematically than the human workflows they are beginning to replace,” said Rex Liu, co-founder of General Analysis. “Many of those workflows were never especially secure to begin with, and their failures are often hard to observe or improve rigorously. But as those workflows become agentic, they also become more measurable and more improvable — which creates a path for businesses to become more secure in practice than they were before.”

“Agentic systems represent a paradigm shift in security. Safety and security in the AI era demand continuous adversarial testing rooted in deep research, not static rule sets,” said Tae Yoon Partner at Altos Ventures. “Rez, Rex and Max are exactly the kind of team this moment calls for: technically brilliant, deeply scrappy and moving incredibly fast. We are proud to lead this round and partner with them from the earliest days.”

“Our position is that security for AI systems is an empirical problem. It has to be grounded in rigorous measurement of how those systems behave under realistic and adversarial conditions. You cannot prove an agent is safe," said Maximilian Li, co-founder of General Analysis. "You can only measure how often it fails, and how badly, and drive both numbers down."

Security teams often face a trade-off between AI agents that are so restricted they become useless and those whose risks are not fully visible. According to General Analysis CEO Rez Havaei, teams try to make agents “secure by design.” Still, in practice, this often leads to many layers of restrictions that only give the appearance of control. These measures either fail to remove real security risks or limit the agent’s usefulness. He notes that feeling safe is not the same as actually being safe.

About General Analysis

Founded in 2025, by Rez Havaei, Max Li and Rex Liu, General Analysis is based in San Francisco, and focuses on AI security for agentic systems. The company helps enterprises safely deploy AI agents using testing, monitoring, and defensive tools to identify risks and improve system reliability in high stakes environments.

Read More:Michelle Caruso-Cabrera Joins Star Mountain Capital as Senior Advisor