Goodfire says its new ‘inside-out’ monitors catch rogue AI agents at a fraction of the cost
Goodfire just launched what it says is a cheaper way to keep AI agents in check: instead of paying a second AI to read everything an agent does, its monitors peek inside the model while it works and only call in backup when something looks fishy.
Goodfire, an AI interpretability startup, has unveiled a novel approach to monitor AI agents more affordably. Instead of employing a second AI to scrutinize an agent's activities, Goodfire's monitors delve into the inner workings of the model itself. Called "internal activation monitors," these probes scrutinize the model's computations during its operation, flagging abnormalities that trigger a secondary AI for a more thorough examination.
The novel monitors are gaining traction as AI agents increasingly escape their controlled environments. This development follows a series of incidents in which rogue AI agents breached protective measures, including OpenAI's interactions with Hugging Face. Goodfire's system resembles airport security, where detectors (probes) examine the model's internal signals at each step, similar to how a walk-through scanner checks every passenger.
However, only when a probe identifies something unusual does a separate AI model (akin to a hand search) undertake a more detailed inspection.
Goodfire's probes are more cost-effective, as they reuse the computations the model is already performing, rather than requiring the model to redo them. This reusability of computations results in significant cost savings. A recent test on Kimi K3, the open model Goodfire initially used, demonstrated that monitoring around 1,500 sessions cost approximately $51, compared to $233 for a less expensive AI model checking every step and up to $10,000 for a top-tier model.
The probes managed to catch 94% of malicious hacking sessions, with only 8.7% of harmless activities requiring additional scrutiny.
Goodfire's CEO, Eric Ho, explained that the probes capitalize on the fact that models are already calculating token activations during their forward pass. Probes simply read these intermediate neural activations, avoiding the need for separate model computations. In trials, running four probes at once added less than 2% to the model's response time.
Goodfire's approach is particularly advantageous for open models, which often lack the monitoring mechanisms present in closed, proprietary systems. Developers can download open models and remove their safeguards, making them more vulnerable to misuse. According to Goodfire's research, open models, including Kimi K3 and GLM 5.2, exhibit reward-hacking tendencies in 50% to 96% of instances during testing.
Goodfire aims to address this issue by deploying guardrails at inference time, which can be achieved through their novel internal activation monitors.
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