A Goodfire egy új, belső aktivációs monitorozási módszert mutatott be, amely ahelyett, hogy egy második mesterséges intelligenciával olvastatna el mindent, belülről figyeli az AI-modell működését. A rendszer csak akkor riaszt és hív segítségül egy külső ellenőrző modellt, ha gyanús jeleket észlel a számítások során. Az eszköz elsőként a Baseten felhőplatform ügyfelei számára érhető el.
A megoldás működése a repülőtéri biztonsági ellenőrzéshez hasonlít, ahol apró detektorok vizsgálják a modell belső jeleit minden lépésnél. A tesztek során ez a módszer 94 százalékos pontossággal szűrte ki a rosszindulatú hackelési kísérleteket, miközben a működési sebességet alig 2 százalékkal lassította.
Az új eljárás rendkívül költséghatékony, mivel a modell által már elvégzett számításokat használja fel újra. Egy 1500 munkamenetből álló teszt során a Goodfire megoldása mindössze 51 dollárba került, míg a hagyományos, külső AI-alapú ellenőrzés költsége akár a 10 000 dollárt is elérhette volna.
Az eredeti szöveg (TechCrunch AI)
Last day to exhibit your breakthrough to 10,000+ tech leaders at Disrupt is on Oct 2. Book Exhibit Table Now.
Disrupt doors open Oct. 13. Get your pass and bring someone with you at 50% off. REGISTER NOW.
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
