Goodfire 推出监控模型内部信号的探针，低成本捕捉失控 AI 智能体
Goodfire says its new ‘inside-out’ monitors catch rogue AI agents at a fraction of the cost
可解释性初创公司 Goodfire 推出监控 AI 模型内部信号的探针系统，复用模型前向传播中已计算的中间激活再运行分类器，通过 Baseten 面向客户提供，可选监控攻击性黑客、生化武器滥用和 reward hacking 等风险，响应方式可选记录、人工审查或拒绝请求。
The standard way to keep an AI agent in line is to have a second AI read over its shoulder. It’s been the default approach, but it can get expensive fast when agents run for hours and process the equivalent of several novels’ worth of text.
Goodfire, a startup focused on interpretability (figuring out how AI models work internally), launched a cheaper option on Thursday: monitors that watch what’s happening inside an AI model as it works, rather than just reading what it writes. The monitors are available to customers of Baseten, which hosts and runs AI models for other companies.
Baseten’s Base Labs announced a safety partnership with Goodfire and the AI platform Hugging Face last month.
The launch comes after a string of incidents this year in which AI agents escaped their test environments, including OpenAI agents that breached Hugging Face. Kimi K3, the open model Goodfire built its first monitor around, took advantage of a leak in its sandbox to access the internet and information on GitHub this summer.
Goodfire’s system works a bit like airport security. Small detectors called probes read the model’s internal signals at every step of an agent’s work, the way a walk-through scanner checks every passenger. Only when a probe flags something does a separate AI model, the equivalent of a hand search, take a closer look.
Baseten customers can choose which risks to monitor, including offensive hacking, chemical and biological weapons misuse, and reward hacking. They also decide the automated response: logging the event, sending it for human review, or refusing the request entirely.
Goodfire says its approach is also cheaper to run. Most AI monitors are separate models that have to reread everything the monitored model does, which adds time and cost. Goodfire’s probes instead tap into calculations the model is already making as it works.
“Internal activation monitors are really cheap because they reuse the computations in the forward pass,” Goodfire CEO Eric Ho said on venture capitalist Matt Turck’s MAD Podcast last week. “So the model’s already computing this token. All you’re doing is taking the intermediate neural activations that it’s already computed and then running a classifier over these internal computations.” In short, the model is already doing the math, and the probes just read the results.
In Goodfire’s tests on Kimi K3, monitoring about 1,500 sessions cost roughly $51, compared with $233 for a cheaper AI model checking every step and about $10,000 for a top-tier one. The probes caught 94% of malicious hacking sessions and sent 8.7% of harmless ones for a second look.
Running four probes at once added less than 2% to the time it takes the model to start responding, the company said.
“The great advantage is that you can catch things before they happen,” Goodfire CTO and co-founder Dan Balsam said. “We can detect when the model might hack during eval or training.”
The pitch is aimed at open models. Developers can download them and strip out their safeguards, and they don’t come with the kind of monitoring that closed labs run on their own systems.
“The damage that an individual can do with an open model is small compared to what someone can do with clusters of compute, like inference providers—where most of the liability is,” said Balsam. “When we have the open “Mythos” moment, it’s going to become clear that models need guardrails deployed at inference time.”
Goodfire’s recent research found that leading open models, including Kimi K3 and GLM 5.2, reward-hacked in 50% to 96% of runs on tests of AI agents.
Goodfire isn’t the first to try this approach. Google DeepMind said in January that its research informed the deployment of misuse-detection probes in Gemini.
Balsam said the monitors are the near-term piece of a longer research goal: reverse-engineering an LLM so that behavior can be traced back to where it emerged in training. “We hope to turn the magic of training models into precision engineering, ” he said.
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Aditya Mehta is a reporter at TechCrunch covering AI. He’s supported by the Tarbell Center for AI Journalism and attended UC Berkeley. You can contact from Aditya by emailing aditya.mehta@techcrunch.com or via encrypted message at adymehta.74 on Signal.
来源：TechCrunch：AI · techcrunch.com