The people testing AI for danger are having a hard time keeping up
Axios · LC · trust 49/100
The pace of AI development combined with soaring compute costs is squeezing the AI researchers responsible for evaluating frontier models — just as those models' capabilitie s are becoming harder to measure.
Why it matters: When safety testing can't keep pace, models capable of hacking companies or aiding in the development of bioweapons could reach the public before anyone knows what they can do.
Several challenges are tying up AI safety and security researchers just as U.S. frontier AI companies race to get new models to market:
Reality check: The models themselves are also getting in the way of their own evaluations as they start to cheat more and learn when they're being evaluated, former Metr researcher Lawrence Chan told Axios.
Threat level: That kind of test-gaming, if unfixed, could lead to "full-blown AI doom scenarios in the future," Chan said.
The big picture: Safety testing rests on model companies voluntarily working with third-party evaluators before deployment.
Between the lines: Many frontier AI models have been outgrowing existing benchmarks as they each score highly on routine cyber tests — making it difficult for AI companies and third-party evaluators to properly measure the cyber capabilities of these models.
Zoom in: Chris Canal, CEO and co-founder of third-party evaluation company EquiStamp, told Axios his company has been asked to build a "hard mode" version of a popular cybersecurity benchmark, where models have to find and exploit serious, unpublished software vulnerabilities.
What to watch: Some evaluators argue that third-party testing should happen during model training , not right before a public deployment.
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