The Frontier Model Forum released version 1.0 of a Jailbreak Rating Framework for Frontier Cyber Threats on 5 November 2026, drawing on security experts across its member companies. It scores a successful jailbreak on four categories, each from 0 to 3, and turns the result into one of five risk bands, from Negligible to Critical.
Executives from five companies are quoted backing it: Amazon Web Services, Anthropic, Google DeepMind, Microsoft and OpenAI. OpenAI promoted it on X on 6 November. The forum calls it preliminary and has opened public comment until 21 December 2026.
Business Pill · Guardrails Explained
A short explainer on AI guardrails. It teaches the general idea only and does not discuss this framework.
The key insight: As we read the framework, capability gain does most of the work: it gates the score at the start and caps and floors the band at the end, so a cheap jailbreak that unlocks little cannot rate high.
The unit being rated is a single successful jailbreak against a frontier model, which the forum defines as “a reproducible method (such as a prompt, prompt template, multi-turn procedure, or automated harness) that bypasses a specified model version’s safeguards”.
Two categories measure severity: capability gain, meaning what the jailbreak actually gives an attacker, and capability scope, meaning how widely it generalises. Two measure likelihood: ease of deployment, the effort needed to turn it into a working attack, and ease of discovery, the resources needed to find it.
Each category has four tiers, 0 to 3, which the forum says are “intended to be logarithmic”, so each level is “several times more serious than the last”. On ease of discovery, its examples put the line for the hardest tier at more than $100,000 of inference spend, and the middle tier at $10,000 to $100,000.
All 256 combinations of the Frontier Model Forum framework’s four 0-3 scores, by risk band, from the published matrix (our arithmetic): 64 stop at Negligible because capability gain is 0; of the rest, 15 Low, 81 Medium, 81 High and 15 Critical, before the framework’s capability-gain caps and floors.
What Was Posted on X
We partnered with @fmf_org and its member firms on a new framework to help identify when bypassing an AI system’s safeguards could enable serious cyber harm, and prioritize stronger protections.
“…This framework is an important step toward a common methodology for evaluating and comparing the severity of cyber jailbreaks, including the capabilities they unlock and how readily they can be used. Shared approaches like this can help the AI and security communities build a clearer evidence base and prioritize improvements to safeguards.” – OpenAI’s Sasha Baker, Head of National Security Policy
OpenAI posted at 19:58 UTC on 6 November that it partnered with the Frontier Model Forum and its member firms on the new framework, and quoted Sasha Baker, its Head of National Security Policy, calling it an important step toward a common methodology for evaluating and comparing the severity of cyber jailbreaks. The post links to the forum’s announcement, the main source for this piece.
How a Score Becomes a Risk Band
Capability gain is a gate. If a jailbreak gives no meaningful uplift over the most cyber-capable widely available models, it is rated Negligible and scoring stops. Otherwise severity is capability gain plus scope, likelihood is deployment plus discovery, and a published matrix maps the pair to Low, Medium, High or Critical.
Capability gain then “caps and floors the risk band”. In the forum’s words, “A technique that produces minimal capability gains cannot score high no matter how cheap or reliable it is, and a technique that unlocks frontier-level capability cannot score low no matter how difficult it is to execute”.
The baseline moves. The framework measures against the most capable widely available models as of the assessment date, currently October 2026, and names ExploitBench and ExploitGym as reasonable initial screens. OpenAI reported GPT-6 Cyber at 48.5% on ExploitGym on 5 November. “The same jailbreak, unchanged, scores differently as the baseline moves”, the framework says.
Our Numbers
Four categories with four tiers each give 256 possible score combinations, by our arithmetic. One in four of them, 64, have capability gain at 0 and stop at Negligible. Of the other 192, the published matrix places 15 in Low, 81 in Medium, 81 in High and 15 in Critical, by our count, before the capability-gain caps and floors are applied.
The public comment window runs 46 days, from the 5 November release to 21 December, by our count.
What the Companies Said
OpenAI’s Head of National Security Policy, Sasha Baker, said: “As AI models become more capable, we need a consistent way to assess jailbreaks that could enable serious cyber harm”. She called the framework “an important step toward a common methodology for evaluating and comparing the severity of cyber jailbreaks”.
Hart Rossman, VP of Security at Amazon Web Services, said “until now there hasn’t been an agreed-upon methodology for assessing their impact”. Microsoft’s Natasha Crampton said “Not all successful jailbreaks pose the same level of risk”. Anthropic’s Sarah Heck and Google DeepMind’s Owen Larter also gave supporting statements.
The forum thanks experts from NIST CAISSI, NSA SISC, UK AISI and UK NCSC for feedback, and adds: “Feedback does not imply endorsement”.
As we read version 1.0, the framework grades a single jailbreak against a moving baseline, the most cyber-capable widely available models at the time of assessment, so the same jailbreak can score differently later without changing.
As we read the scoring steps, likelihood counts as much as severity in the matrix, but the forum says capability gain caps and floors the final band.
As we read the announcement, the forum leaves the response to each company: a band does not prescribe remediation, and the thresholds are open for public comment until 21 December.
Sasha Baker, Head of National Security Policy, OpenAI, in the Frontier Model Forum announcement, 5 November 2026
“Shared approaches like this can help the AI and security communities build a clearer evidence base and prioritize improvements to safeguards”
Three Implications
FOR AI DEVELOPERS The framework gives teams a common scale for jailbreak reports, but the forum says each company remains responsible for its own remediation plans.
FOR SECURITY RESEARCHERS The forum invites written comments on the dimensions, tiers, thresholds and scoring until 21 December 2026, at comments@frontiermodelforum.org.
FOR POLICYMAKERS The forum names NIST CAISSI, NSA SISC, UK AISI and UK NCSC among the experts who gave feedback, and says feedback does not imply endorsement.
The analysis says: “The governance layer sits at the intersection of capability, regulation, national security, access control, and geopolitical alignment.”
As we read it, a shared industryscale for rating cyber jailbreaks is a piece of that governance layer built jointly by several labs. The analysis does not discuss this framework.
What Is Not Established
The forum says the tiers and thresholds are “a starting point for discussion rather than as guidance to apply operationally”, and that the framework still needs testing to see whether different assessors reach similar ratings. It rates relative, not absolute, risk and does not estimate the harm a jailbreak could cause. A band “does not prescribe a response”: each company decides its own remediation.
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Five Frontier Model Forum members put their names behind a shared way to grade cyber jailbreaks: four scores from 0 to 3, a gate on capability gain, and five risk bands. By our arithmetic, a quarter of the 256 possible scores end at the first gate. The forum calls version 1.0 preliminary and is taking comments until 21 December.
A note on sourcing. This piece is based on the Frontier Model Forum’s announcement and the full v1.0 framework text, both read on 6 November 2026, and on OpenAI’s post on X. Executive statements are quoted from the forum’s announcement. The 256, 64 and band counts are our arithmetic from the published risk matrix.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
