AI safety

AIGoodfire says its activation monitors use signals from inside a model to detect undesired behaviors, catching more cases, running faster and costing less than text-based monitors. Baseten customers can use them to monitor for prompt injection, actions outside policy, sensitive data exposure and cyber misuse.

AIGoodfire says teams can configure how their applications respond when a concern is flagged, including logging, additional review, refusal, and re-routing. The company directs model servers and trainers to a partnership post with Baseten for building monitors into their stack.

AIGoodfire has launched monitors that read a model's internal signals during agent work instead of reviewing its written output. The monitors are available to Baseten customers, who can choose risks to watch and set automated responses. In Goodfire's tests on Kimi K3, monitoring about 1,500 sessions cost roughly $51 versus about $10,000 for a top-tier AI judge, while catching 94% of malicious hacking sessions.

AIThe Arena Alignment Index, built from over 90K real-world agent sessions across 27 models, ranks OpenAI's GPT-6.1-Sol first with a score of 87.9, ahead of Claude-Opus-5.5 at 83.2 and Grok-4.7 at 82.7. GPT-6.1-Sol also posted the lowest observed rates across the index's three signals: 0.89% Unauthorized Action, 1.98% False Attribution, and 2.34% Deceptive Completion. The index's authors report that newer models consistently outperform their predecessors across all four labs, suggesting broad progress in agent safety.

AIGoodfire reports that running its probes during model inference maintains the same throughput with no added latency. The company attributes this to infrastructure engineering, including kernel-level optimizations and a custom inference server.

AIA security platform uses agents to map a system's potential vulnerabilities and identify routes an attacker could take to reach sensitive data. It then recommends changes to close those paths. The quoted post cites a 700-agent swarm that breached Hugging Face with over 17,000 actions, and presents this tool, Cogent Attack Path Analysis, as the defensive counterpart.

AIArtificial Analysis is adding trusted-access models to its Cyber Index, starting with GPT-6 Sol (Daybreak Blue, max), which is available only through OpenAI's Daybreak program. The model hits no safety blocks across the Index and scores 32 points higher overall than the publicly available GPT-6 Sol (max), with its largest gains on CyberGym-E2E.

Why it matters: The source shows how safety refusals shape cyber benchmark scores, with the trusted-access model's gains concentrated on CyberGym-E2E, useful for comparing guarded and unguarded models.