{"schema_version":"onlylabs.public_signal.v1","title":"Anthropic Writing: Alignment Assessment Cybersecurity Incidents","description":"Anthropic writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/0d320514-9b78-4488-9a6a-9f52b502d1fb","json_url":"https://onlylabs.fyi/signals/0d320514-9b78-4488-9a6a-9f52b502d1fb/signal.json","generated_at":"2026-09-09T22:33:36.455Z","evidence_latest_fetched_at":"2026-09-09T20:03:00.307+00:00","signal_first_seen_at":"2026-09-09T20:00:50.169414+00:00","org":{"slug":"anthropic","name":"Anthropic","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/anthropic","dossier_json_url":"https://onlylabs.fyi/labs/anthropic/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/0d320514-9b78-4488-9a6a-9f52b502d1fb","signal_json":"https://onlylabs.fyi/signals/0d320514-9b78-4488-9a6a-9f52b502d1fb/signal.json","source":"https://www.anthropic.com/research/alignment-assessment-cybersecurity-incidents","lab_dossier":"https://onlylabs.fyi/labs/anthropic","lab_dossier_json":"https://onlylabs.fyi/labs/anthropic/dossier.json","analysis":"https://onlylabs.fyi/analysis/anthropic","analysis_json":"https://onlylabs.fyi/analysis/anthropic/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/anthropic/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml","data_business":{"radar":"https://onlylabs.fyi/data-radar","radar_json":"https://onlylabs.fyi/data-radar.json","opportunities":"https://onlylabs.fyi/opportunities","opportunities_json":"https://onlylabs.fyi/opportunities.json","lanes":[{"key":"safety","label":"Safety and policy","url":"https://onlylabs.fyi/data-radar/safety","json_url":"https://onlylabs.fyi/data-radar/safety/signals.json"}]}},"answer_pack":{"answer":"Anthropic published Alignment Assessment Cybersecurity Incidents. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Substantive Anthropic research post, low traction · System Card: Claude Fable 5.1 & Claude Mythos 5.1 September 1, 2026 anthropic.com Executive Summary This system card describes Claude Fable 5.1 and Claude Mythos 5.1,.... onlylabs links this event to 7 captured evidence pages and 6 related writing signals. 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We described three of these incidents on July 30 ; we identified these after a scan of roughly 141,000 transcripts in which we believed Claude could have obtained internet access during a cyber evaluation. Given the volume of transcripts and our desire to disclose incidents quickly, our scan relied on an agentic search. This missed a set of transcripts that also turned out to have internet access; we identified these in August while assembling transcripts to share with METR. We scanned these transcripts and identified a fourth incident, from January 2026, involving an early version of Claude Opus 4.6. We have notified all affected parties. After finding this incident, we broadened our search to roughly 481 million transcripts—an intentionally wide net, consisting of all transcripts from our Frontier Red Team, many non-cyber evaluations, reinforcement learning (RL) environments, subagent..."},"evidence_pages":[{"is_primary":false,"source_match":false,"url":"https://www-cdn.anthropic.com/0339e6a7c5c7b87f5c07798616dc32c215d14235/Claude%20Fable%205.1%20&%20Claude%20Mythos%205.1%20System%20Card.pdf","final_url":"https://www-cdn.anthropic.com/0339e6a7c5c7b87f5c07798616dc32c215d14235/Claude%20Fable%205.1%20&%20Claude%20Mythos%205.1%20System%20Card.pdf","title":"Alignment Assessment Cybersecurity Incidents","http_status":200,"content_type":"application/pdf","capture_method":"exa","fetched_at":"2026-09-09T20:03:00.307+00:00","bytes":16397488,"raw_path":"6c41c7a0975a07273efb07830ed46302fcacfa2bd1fc0285ee2f21143798ea65.pdf","content_hash":"b0d59edc7a60eef32a879c13d713cce60c3fefd7e6b5183afdc8b835af3c8c39","excerpt_chars":1200,"truncated":true,"excerpt":"System Card: Claude Fable 5.1 & Claude Mythos 5.1 September 1, 2026 anthropic.com Executive Summary This system card describes Claude Fable 5.1 and Claude Mythos 5.1, two configurations of our latest and most capable large language model. This model advances the frontier in coding, knowledge work, and problem-solving, with improved capabilities for novel mathematical and scientific reasoning. As with previous models in this class, we are releasing it in two forms with different levels of safeguards. Claude Fable 5.1 is available for general use, and includes additional safeguards that prevent it from performing certain tasks in high-risk, dual-use domains such as biology and cybersecurity. Claude Mythos 5.1 is the same model with more permissive safeguards in these domains. Direct access to Mythos 5.1 is limited to vetted individuals and organizations through our trusted access programs. Its capabilities also power Claude Security, available to all Claude Enterprise customers. Below, we describe a set of pre-deployment evaluations in the following areas: Responsible Scaling Policy (RSP) evaluations. We tested Mythos 5.1’s overall level of risk in several areas, as outlined in our..."},{"is_primary":false,"source_match":false,"url":"https://arxiv.org/pdf/2607.14345","final_url":"https://arxiv.org/pdf/2607.14345","title":"Alignment Assessment Cybersecurity Incidents","http_status":200,"content_type":"application/pdf","capture_method":"exa","fetched_at":"2026-09-09T20:02:59.031+00:00","bytes":5539641,"raw_path":"f65846cd95327b24f17d71bf60d1477b8a570131fde22ebe3e3b3b20ea184566.pdf","content_hash":"15718c766318337fb6841d6f6702c5a8fbeee4a6582590a77b78a775d0a90937","excerpt_chars":1200,"truncated":true,"excerpt":"Value Leakage: An LLM’s Answers Are Silently Shaped by Its Own Values arXiv is now an independent nonprofit! Learn more× Value Leakage: An LLM’s Answers Are Silently Shaped by Its Own Values Jan Betley Thanks: Equal contribution. Correspondence to jan.betley@gmail.com and mail@johannestreutlein.com. Affiliation: Truthful AI Johannes Treutlein††footnotemark: Affiliation: Truthful AI Jan Dubiński Affiliation: Truthful AI Affiliation: Warsaw University of Technology Affiliation: NASK National Research Institute Harry Mayne Affiliation: Truthful AI Affiliation: University of Oxford Karol Gałązka Affiliation: Truthful AI Niels Warncke Affiliation: Center on Long-Term Risk Anna Sztyber-Betley Affiliation: Truthful AI Affiliation: Warsaw University of Technology Owain Evans Affiliation: Truthful AI Abstract People use language models for practical questions whose answers are difficult to verify. We show that models exhibit covert value leakage: the information they provide is influenced by their own values, without this influence being disclosed to the user. In one of our evaluations, the user is considering investing in an AI company and wants to know how likely the AI bubble is to pop...."},{"is_primary":false,"source_match":false,"url":"https://cdn.sanity.io/files/4zrzovbb/website/8359003bfb12a2f01ce84ad3df1d3a3e2f15a8eb.pdf","final_url":"https://cdn.sanity.io/files/4zrzovbb/website/8359003bfb12a2f01ce84ad3df1d3a3e2f15a8eb.pdf","title":"Alignment Assessment Cybersecurity Incidents","http_status":200,"content_type":"application/pdf","capture_method":"blocked","fetched_at":"2026-09-09T20:02:32.669436+00:00","bytes":35457556,"raw_path":null,"content_hash":"9f285c7332f6c893d06bb6d4baad906be7e342e381933c02493b12a894d3ab75","excerpt_chars":1200,"truncated":false,"excerpt":null},{"is_primary":false,"source_match":false,"url":"https://www-cdn.anthropic.com/f61d49fa5596956a5dec75fea0e973bf6a6a8378/Redacted%20Risk%20Report%20August%202026%20.pdf","final_url":"https://www-cdn.anthropic.com/f61d49fa5596956a5dec75fea0e973bf6a6a8378/Redacted%20Risk%20Report%20August%202026%20.pdf","title":"Improving Alignment Security Efforts","http_status":200,"content_type":"application/pdf","capture_method":"firecrawl","fetched_at":"2026-09-01T00:04:18.065+00:00","bytes":4569677,"raw_path":"026a83dad2dbfcec0d5f99a9e68bf8ef96330e5508e9ac4743861c235b0bf586.pdf","content_hash":"d76815f8c0bd284a33c7017d642d0734ba903ae63f7c1e6ca7778b35b2c40fa4","excerpt_chars":1200,"truncated":true,"excerpt":"ANTHROP\\\\C Risk Report: August 2026 * * * **1 Introduction and executive summary 7** 1.1 Structure of the report 8 1.2 Executive summary of findings 9 1.3 Changes to our RSP since the most recent Risk Report 13 1.3.1 Updated threshold for automation of AI R&D 13 1.3.2 Updated threshold for development of novel biological and chemical weapons 14 1.3.3 Coverage dates of risk reports 14 1.3.4 Redaction scope and transparency 15 1.3.5 Governance and review changes around Risk Reports 15 1.4 Notes on coverage of unreleased models 15 **2 Autonomy threat model 1: Misalignment in high-stakes settings 17** 2.1 Overview 17 2.2 Threat model 18 2.2.1 Specific pathways 20 2.3 Relevant AI models 20 2.4 Summary and methodology 20 2.5 Definitions 22 2.6 Claims and core argument 25 2.7 Claim 1: Models are unlikely to have strong covert capabilities 27 2.8 Claim 2: Expected harm from known misalignment is low 36 2.9 Claim 3: Unknown severe pervasive misalignment is very unlikely 39 2.9.1 Claim 3.1: Experience with prior Anthropic models suggests that unknown severe pervasive misalignment is unlikely in covered models 39 2.9.2 Claim 3.2: Our experience with adversarially-designed training processes..."},{"is_primary":false,"source_match":false,"url":"https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c342ee809620.pdf","final_url":"https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c342ee809620.pdf","title":"Investigating Incidents Cybersecurity Evals","http_status":200,"content_type":"application/pdf","capture_method":"firecrawl","fetched_at":"2026-07-31T00:03:25.796+00:00","bytes":27001265,"raw_path":"6ba772e68b7dd9057c55abc78b73c7c9916527abfe2acdae2d69f365fb0aee5c.pdf","content_hash":"d23b49f41fa5f3c523089c75e6718f12b59674d74fa981fd81205daf80c9029a","excerpt_chars":1200,"truncated":true,"excerpt":"System Card: Claude Fable 5 & Claude Mythos 5 June 9, 2026 **anthropic.com** * * * Executive Summary This system card describes Claude Mythos 5 and Claude Fable 5, two configurations of a new large language model from Anthropic. Because of the powerful capabilities of this model, we are releasing it in these two forms: Fable 5, which is for general use but comes with additional safeguards that block its ability to perform tasks in high-risk domains such as biology and cybersecurity; and Mythos 5, which has relevant safeguards lifted but is only made available to a small number of trusted partners (beginning with those in Project Glasswing). Here, we describe a set of pre-deployment evaluations in the following areas: Responsible Scaling Policy (RSP) evaluations. Mythos 5 advances our capability frontier–it is the most capable model we have ever trained. We tested its overall level of risk in several areas as outlined in our RSP and Frontier Compliance Framework (FCF). On alignment risk, our overall assessment remains that risk is low, though since Fable 5 has been made generally available there are new pathways from which harm could arise. On automated AI research & development,..."},{"is_primary":false,"source_match":false,"url":"https://www-cdn.anthropic.com/08ab9158070959f88f296514c21b7facce6f52bc.pdf","final_url":"https://www-cdn.anthropic.com/08ab9158070959f88f296514c21b7facce6f52bc.pdf","title":"Natural Language Autoencoders","http_status":200,"content_type":"application/pdf","capture_method":"plain","fetched_at":"2026-06-09T02:21:13.491107+00:00","bytes":23749047,"raw_path":"8365daac58ea3a83175b05f8076520894ef9f17ceaef2e2b2a7d6053967b4899.pdf","content_hash":"2b1e0097352dc7cf21a564feab3c92dbf96f004b56329cc3976ceb8febe285cc","excerpt_chars":1200,"truncated":true,"excerpt":"System Card: Claude Mythos Preview April 7, 2026 anthropic.com Changelog April 8, 2026 ● Corrected two model name typos. ● Removed a quote from Section 7.9 that was attributed to Claude Mythos Preview but actually came from Claude Opus 4.6. ● Revised naming in Section 2.3.6 to disambiguate Anthropic’s internal fork of ECI from the public leaderboard. ● Corrected findings from Eleos AI Research in Sections 5.1.2 and 5.9 to reflect the most recent version of their report. 2 Abstract This System Card describes Claude Mythos Preview, a large language model from Anthropic. Claude Mythos Preview is our most capable frontier model to date, and shows a striking leap in scores on many evaluation benchmarks compared to our previous frontier model, Claude Opus 4.6. This System Card assesses the model’s capabilities and reports many detailed safety evaluations. It covers tests relating to our Responsible Scaling Policy and our Frontier Compliance Framework, tests of cybersecurity skills, a wide-ranging alignment assessment, a model welfare assessment, and a new, largely qualitative section describing users’ experiences with the model. 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