Goodfire推出基于内部激活的AI智能体监控系统，成本远低于传统输出审阅
Goodfire says its new ‘inside-out’ monitors catch rogue AI agents at a fraction of the cost
Goodfire推出基于模型内部激活的AI智能体监控系统，在模型前向计算中插入小型分类器读取风险信号，绕过对输出的二次审阅。系统已在Baseten上线，监测越权入侵、化学与生物武器滥用与奖励黑客等风险，响应方式包括记录、人工复核或直接拒绝。
原文给出了内部激活探针与外层审阅模型的成本对比和检出率数据，读者可以据此评估它能否替代传统输出监控方案。
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 · techcrunch.com