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{
  "url": "https://arxiv.org/abs/2610.13928",
  "max_chars": 4000
}

{"ok":true,"url":"https://arxiv.org/abs/2610.13928","text":"[2610.13928] AI Models Can Provably Hide Arbitrary Capabilities\nSkip to main content\nSearch\nSubmit\nDonate\nLog in\nSearch arXiv\nPress Enter to search &middot; Advanced search\n-->\nComputer Science > Machine Learning\narXiv:2610.13928 (cs)\n[Submitted on 9 Oct 2026 ( v1 ), last revised 14 Oct 2026 (this version, v2)]\nTitle: AI Models Can Provably Hide Arbitrary Capabilities\nAuthors: Andis Draguns , Sumeet Ramesh Motwani , Raymond Douglas , Christian Schroeder de Witt\nView a PDF of the paper titled AI Models Can Provably Hide Arbitrary Capabilities, by Andis Draguns and 3 other authors\nView PDF\nHTML (experimental)\nAbstract: Capability evaluations aim to surface dangerous model behaviors before deployment, but their reliability depends on the assumption that hidden capabilities can be elicited. We show this assumption does not hold under adversarial conditions by constructing backdoor attacks that embed encrypted neural networks within host model weights, decrypting and executing them only upon receiving a secret trigger. Using digital lockers, these hidden circuits remain provably hard to elicit and interpret under cryptographic assumptions, even given full access to model weights, extending prior work from hiding fixed strings to arbitrary computations. We implement our proof of concept constructions in PyTorch and evaluate elicitation through fine-tuning, weight noising, gradient-based trigger search, and latent adversarial training, using a small chemical reaction prediction task as a proxy for CBRN-relevant capabilities. Our results demonstrate resistance to these methods and, together with our cryptographic guarantees, reveal a limit of elicitation-based capability audits. We release our models to support the improvement of defenses.\nComments:\n22 pages, 6 figures, 3 tables. Accepted at NeurIPS 2026\nSubjects:\nMachine Learning (cs.LG) ; Cryptography and Security (cs.CR)\nCite as:\narXiv:2610.13928 [cs.LG]\n(or\narXiv:2610.13928v2 [cs.LG] for this version)\nhttps://doi.org/10.48550/arXiv.2610.13928\nFocus to learn more\narXiv-issued DOI via DataCite\nSubmission history\nFrom: Andis Draguns [ view email ] [v1]\nFri, 9 Oct 2026 17:55:06 UTC (878 KB)\n[v2]\nWed, 14 Oct 2026 17:58:11 UTC (541 KB)\nFull-text links:\nAccess Paper:\nView a PDF of the paper titled AI Models Can Provably Hide Arbitrary Capabilities, by Andis Draguns and 3 other authors\nView PDF\nHTML (experimental)\nTeX Source\nview license\nAdditional Features\nAudio Summary\nCurrent browse context:\ncs.LG\n< prev\n|\nnext >\nnew\n|\nrecent\n| 2026-10\nChange to browse by:\ncs\ncs.CR\nReferences & Citations\nNASA ADS\nGoogle Scholar\nSemantic Scholar\nexport BibTeX citation\nLoading...\nBibTeX formatted citation\n&times;\nloading...\nData provided by:\nBookmark\nBibliographic Tools\nBibliographic and Citation Tools\nBibliographic Explorer Toggle\nBibliographic Explorer ( What is the Explorer? )\nConnected Papers Toggle\nConnected Papers ( What is Connected Papers? )\nLitmaps Toggle\nLitmaps ( What is Litmaps? )\nscite.ai Toggle\nscite Smart Citations ( What are Smart Citations? )\nCode, Data, Media\nCode, Data and Media Associated with this Article\nalphaXiv Toggle\nalphaXiv ( What is alphaXiv? )\nLinks to Code Toggle\nCatalyzeX Code Finder for Papers ( What is CatalyzeX? )\nDagsHub Toggle\nDagsHub ( What is DagsHub? )\nGotitPub Toggle\nGotit.pub ( What is GotitPub? )\nHuggingface Toggle\nHugging Face ( What is Huggingface? )\nScienceCast Toggle\nScienceCast ( What is ScienceCast? )\nDemos\nDemos\nReplicate Toggle\nReplicate ( What is Replicate? )\nSpaces Toggle\nHugging Face Spaces ( What is Spaces? )\nSpaces Toggle\nTXYZ.AI ( What is TXYZ.AI? )\nRelated Papers\nRecommenders and Search Tools\nLink to Influence Flower\nInfluence Flower ( What are Influence Flowers? )\nCore recommender toggle\nCORE Recommender ( What is CORE? )\nIArxiv recommender toggle\nIArxiv Recommender\n( What is IArxiv? )\nAuthor\nVenue\nInstitution\nTopic\nAbout arXivLabs\narXivLabs: experimental projects with community collaborators\narXivLabs is a framework that allows collaborators to d","chars":4000,"total_chars":4777,"offset":0,"truncated":true,"next_offset":4000}