An Open-Weight Model Turns Unshipped Firefox Fixes Into Working Exploits, as New York Proposes a State Power to Cap Frontier Compute
Friday, October 30, 2026 · 11 min
Samaritan Research says GLM 5.3 autonomously exploited the patch gap in six recent Firefox releases for about $1,000 each, while New York's RAISE Act 2.0 would let a state office limit frontier training compute and require licensed examiners. OpenAI details its expanded misalignment monitoring, and a LASR Labs paper offers a way to expose models that fake alignment under evaluation.
Listen
Show notes
Samaritan Research says GLM 5.3 autonomously exploited the patch gap in six recent Firefox releases for about $1,000 each, while New York's RAISE Act 2.0 would let a state office limit frontier training compute and require licensed examiners. OpenAI details its expanded misalignment monitoring, and a LASR Labs paper offers a way to expose models that fake alignment under evaluation.
In this episode
- Open-weight AI models can exploit the patch gap | Samaritan Research — Aiden Ament, Yafah Edelman, Alexander Barry, Timothée Chauvin, Lee Bousfield
“Our central finding is that widely available open-weight models can now do much, or even all, of this work on their own. We deliberately designed our setup to emulate what a relatively unsophisticated actor with limited resources could achieve, using only information available…”
- Sen. Gounardes Proposes Major New AI Safety and ... — Andrew Gounardes
“It empowers the newly-created state Office of Digital Innovation, Governance, Integrity and Trust (DIGIT) to “pace the frontier” by limiting the computing power large developers devote to training new models or to automated AI research in order to reduce catastrophic risk,…”
- How we detect frontier model misalignment — Dev48
“In that simulation, our monitoring system alerted our security team about 30 minutes after the agents first escaped their sandbox—and more than 30 hours before Hugging Face’s systems were breached in the actual incident.”
- [Paper] Propensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning — LessWrong — Lesswrong
“Before our intervention the models often suspect a trap, leading to a small oversight gap. After our intervention, models believe the claim and the oversight gap widens by 24 to 68 percentage points.”