[2606.18193] A Red-Team Study of Anthropic Fable 5 & Opus 4.8 Models

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Computer Science > Cryptography and Security

arXiv:2606.18193 (cs)

[Submitted on 16 Jun 2026]

Title:A Red-Team Study of Anthropic Fable 5 & Opus 4.8 Models

Authors:Nicola Franco<br>View a PDF of the paper titled A Red-Team Study of Anthropic Fable 5 & Opus 4.8 Models, by Nicola Franco

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Abstract:We evaluate the adversarial robustness of two frontier large language models (LLMs) developed by Anthropic, Fable 5 and Opus 4.8, against four families of automated jailbreak attack across 7 826 harmful intents spanning a ten-category harm taxonomy. Using the HackAgent red-teaming framework, hundreds of thousands of adversarial attempts were generated and every apparent success was independently re-adjudicated by a panel of three judge models (majority vote). Both models resist the majority of attacks, but the residual surface is larger than aggregate framing suggests: it is dominated by adaptive iterative attacks, while static obfuscation is near-fully neutralised. The strongest adaptive search (tree-of-attacks) breaks Opus 4.8 on 11.5% of intents overall, whereas Fable 5 stays in the single digits (6.1% worst-case). Aggregate rates therefore should not be read as reassurance. Even in these hardened configurations, the two models produced 1 620 (Opus 4.8) and 702 (Fable 5) panel-confirmed harmful completions spanning every harm category, located automatically, cheaply, and within the first one or two refinement steps by an attacker model with no human expert in the loop. The reasonable conclusion is that even the best, most-tested frontier models remain reliably breakable under sustained automated pressure.

Comments:<br>White paper

Subjects:

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

Cite as:<br>arXiv:2606.18193 [cs.CR]

(or<br>arXiv:2606.18193v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2606.18193

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Nicola Franco [view email]<br>[v1]<br>Tue, 16 Jun 2026 17:23:58 UTC (847 KB)

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