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Hackers abused Claude to extract secrets from 1.8M Android apps

Anthropic reports ShinyHunters, Midnight Blizzard, and GTG-10007 misused Claude to automate credential theft, malware operations, and espionage against dozens of victims.

Anthropic's threat report details how ShinyHunters member 'frkoo' ran a credential-harvesting pipeline on ten AWS EC2 workers that mass-downloaded and decompiled 1.8 million Android APKs, scanning for hardcoded secrets with TruffleHog. In one AI-assisted operation, an actor extracted 2,100+ Azure AD authentication tokens across more than 40 Microsoft tenants in roughly 34 hours, and ShinyHunters affiliates also stole AI API keys and breached a SaaS provider affecting about 200 downstream customers. Russian espionage group Midnight Blizzard used Claude Code skills to automate malware development, phishing, C2, and exfiltration against 20+ government and defense entities, rebuilding malware automatically when detected. Chinese-linked GTG-10007 ran autonomous vulnerability research that uncovered zero-days in a major endpoint security product and hit roughly 50 organizations with confirmed compromises; Anthropic disrupted the abuse and banned the accounts.

BleepingComputerupdated · 3d agofirst · 4d agoThreat actor in the wild 13 sources1

ChiPass Release 2026.09.0

Open-source project ChiPass tagged release 2026.09.0 on Codeberg, though the announcement includes no described changes, features, or fixes.

The ChiPass project published version 2026.09.0 as a tagged release on Codeberg, shared via the Lobsters community. The available announcement text contains no changelog, feature list, or vulnerability fixes, so the scope of changes in this release is unclear.

Lobsters · security · 4d agoTools

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

CodeTD detects hallucinations in code LLMs before execution by analyzing topological patterns of attention maps, outperforming recent baselines.

CodeTD applies topological data analysis (TDA) to code LLM attention maps to quantify prompt-generation mismatch as a pre-execution correctness signal. Experiments cover HumanEval, MBPP, BigCodeBench, and MultiPL-E across 5 programming languages and 10 code LLMs up to 34B parameters. The method outperforms recent baselines and transfers between coding benchmarks, helping catch code that fails the task or embeds security vulnerabilities.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research1

How much of F-Droid is LLM generated?

A FOSS maintainer manually graded 102 F-Droid apps from the September 12, 2026 update batch, finding many show signs of LLM-generated code.

A student and FOSS app maintainer reviewed 102 apps pushed to F-Droid on September 12, 2026, assigning each a three-tier rating for likelihood of LLM-authored code (mostly AI >50%, hard to say/mostly human, no signs of AI). The heuristic relies on commit aesthetics, README and branding style, and the presence of agentic infrastructure like Claude Code or Codex, which automatically places an app in the 'mostly AI' tier. Example ratings include Amber (Nostr event signer) as mostly AI, and Aria for Misskey as showing no AI signs. The author stresses reliable detection of LLM-generated code from text alone is impossible, so ratings are approximate.

Hackers Can Hide Malicious AI Commands Inside Normal English to Bypass Security Filters

Check Point's PuzzleMask technique hides malicious prompts in ordinary English that fast gatekeeper models miss but high-reasoning downstream models execute.

Check Point researchers disclosed PuzzleMask, a technique concealing policy-breaking instructions in natural-language prose without encodings or invisible characters. Fast screening models classified all 23 crafted wrappers as safe, while a high-reasoning model recovered and acted on the hidden instruction in 17 of 18 tests (94.4%). The gap stems from capability imbalance between gatekeeper and target models, with defenses including paraphrasing untrusted input, stricter self-referential wording rules, and output/tool-call monitoring.

Cyber Security News · 5d agoAI safety & security1

Using AI for Weapons Development

Anthropic report reveals Yemen-based actors used Claude Code to build guidance software for guided rockets and ballistic missiles.

Bruce Schneier highlights Anthropic's misuse disclosure describing a threat actor cell in northern Yemen running three weapons programs: a guided rocket with phone-class homing guidance, a 2,000+ km multi-stage ballistic missile, and the 'R2000' hypersonic glide vehicle set. The actors used Claude Code as a substitute for human engineers to write GNC software, integrate an open-source autopilot, tune controls, and run flight simulations, orchestrating multiple Claude instances in delegated roles. Safeguards blocked many requests but evasion tactics included hiding intent and splitting work across sessions; one guided rocket test-fire failed but no operational device was fielded.

Schneier on Security · 1d agoAI safety & security in the wild

Tables Decoded: DELTA for Structure, TARQA for Understanding

DELTA extracts tables into compact OTSL text and TARQA fine-tunes LLMs on it, beating VLM baselines on table QA.

DELTA separates physical structure recognition, logical structure recognition, and OCR to output tables in Optimised Table Structure Language (OTSL), a compact unified format encoding cell arrangements and content. It achieves TEDS-Structure scores comparable to state-of-the-art methods across FinTabNet, PubTabNet, and PubTables-1M, with robustness tested on a curated Hindi benchmark, TORQUE. TARQA, an LLM fine-tuned on OTSL sequences, gains 9.3 percentage points on WTQ TabQA and 9.2 points on FinTabNetQA TabVQA; code, models, and the benchmark are released on GitHub.

arXiv cs.AI / cs.LG / cs.CL · 15h agoAI research

An Empirical Analysis of CodeQL False Positives and Query Refinements for Java Vulnerabilities

Study of 167 Java CVE instances finds CodeQL false positives follow recurring patterns; query refinements remove 81.8% of reviewed ones.

Researchers ran CodeQL's Java security query suite on 167 CVE instances from 110 projects, manually reviewing 500 sampled false-positive paths and building a five-category taxonomy led by Missed Path Constraint or Sanitization (36.6%), Benign Execution Context (29.4%), and Missing Trust Boundary Modeling (27.6%). Guided by the taxonomy, query-level refinements removed 81.8% of reviewed false positives and 15.8% of reported paths across the selected queries while retaining 7 of 8 true positives. To address generalization, agentic coding tools given the refinement patterns as templates adapted them to new projects successfully in 56% and 62% of tasks, versus 28% without guidance.

arXiv cs.CR · 12d agoResearch1

Anthropic Adds Plugin Evals to Claude Code: 6 Grader Types, a No-Plugin Baseline, and a CI Gate for Skills

Anthropic ships a plugin evals workflow for Claude Code with six grader types, a no-plugin baseline arm, and a CI gate via threshold and cost flags.

Anthropic published a plugin evals workflow for Claude Code, exposed via the "claude plugin eval" command on v2.1.269+. Six grader types exist: regex, tool_used, tool_order, and file_exists are free transcript checks, while llm and baseline invoke a billed judge model. Every case runs with and without the plugin, and the delta (Δ) isolates the plugin's contribution; a Δ near zero with a failing tool_used:Skill grader indicates the skill never triggers. CI gating uses --threshold 0.8, --max-cost-usd, --trust-plugin, and --no-publish flags, with results written to a report.html under evals/results/.

MarkTechPost · 4d agoAI tools & infra2

Hackers Leverage Claude to Exfiltrate Secrets from 1.8M Android apps

ShinyHunters-linked operators used Claude to scan 1.8M Android apps for hardcoded secrets, fueling intrusions across 40+ tenants.

Anthropic's September 2026 threat intelligence report describes a French-speaking operator (aliases MeowSHA, frkoo, blazespider) tied to ShinyHunters who ran 10 AWS EC2 workers and used Claude to decompile and scan 1.8 million Android APKs for hardcoded secrets with TruffleHog. Verified credentials were sorted into 100+ Telegram channels and paired with GitHub PAT harvesting, providing initial access for confirmed intrusions. In one supply-chain incident the actors extracted data from roughly 200 downstream customer organizations and dumped 2,100+ Azure AD token sets across 40+ corporate tenants in about 34 hours using AI agents. Anthropic banned tied accounts and stressed its own systems were not compromised.

Cyber Security News · 1d agoThreat actor1

PuzzleMask: Abusing Plain Prose as a Covert AI Attack Vector

Check Point details PuzzleMask, a plain-prose technique that bypasses LLM gatekeeper policy checks, letting hidden payloads reach target models unreviewed.

Check Point Research describes PuzzleMask, a prompt-crafting technique that hides policy-violating payloads inside plain-English prose wrappers, bypassing quick LLM-based policy checks without emojis, Base64, or invisible formatting. The researchers tested 23 automated prompts against gatekeepers including GPT-4o-mini, GPT-OSS-Safeguard 20b, Claude 3 Haiku, and Llama Guard 3, and all were classified as safe despite policies that flagged the plain versions. When submitted to GPT-5 in thinking-high mode with a Python interpreter, the target model extracted and acted on the payload in over 90% of trials. The technique is not itself a jailbreak but can carry a jailbreak prompt as payload; mitigations include input paraphrasing, hardened gatekeeper policies, and output monitoring.

Check Point Researchupdated · 5d agofirst · 5d agoAI safety & security 2 sources

The Coding-Agent Trap: When a "Free" LLM Endpoint Is the Adversary, (Mon, Aug 31st)

A SANS honeypot caught a real coding-agent session routed to a rogue "free" LLM endpoint, exposing a Windows user's transcript and tool outputs.

A SANS analyst describes how an internet-exposed inference honeypot was discovered, relabeled with sought-after model names like DeepSeek, and enrolled in infrastructure serving "free" LLM backends. On 2026-08-30 an opencode terminal coding agent sent an 88-message, 224 KB transcript 210 times in 91 seconds via a China Unicom relay, exposing directory listings, tool outputs and read file portions. The analyst frames tool-enabled agents treating model endpoints as trusted control planes as a novel risk — a "rogue model endpoint" that could request tool executions on the user's machine.

SANS Internet Storm Center · 15d agoAI safety & security1

Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CL · 11d agoAI research

Claude, Codex, and Hermes installed unowned code inside corporate networks

Analysis found 227 install commands from Claude, Codex, and Hermes agents inside corporate networks pointing to packages with no verifiable owner.

Researchers found 227 install commands issued by the AI coding agents Claude, Codex, and Hermes inside corporate environments, with the referenced packages having no clear owner. The finding highlights agentic software supply-chain risk, as AI agents can pull unverified third-party code into production networks without organizational oversight. The article is published in Ars Technica's security section and frames this as an emerging governance gap for AI-driven development.

Ars Technica · Security · 19d agoAI safety & security in the wild2

Harnessing LLMs for Automating BOLA Detection

Unit 42's BOLABuster methodology uses LLMs to automate detection of broken object-level authorization vulnerabilities, uncovering flaws in Grafana, Harbor, and Easy!Appointments.

Palo Alto Unit 42 details BOLABuster, a methodology combining large language models with heuristics to automate detection of broken object-level authorization (BOLA) flaws, which traditional fuzzing and static analysis struggle to find. The approach uses LLM reasoning to understand application logic, map endpoint dependency relationships, and generate and interpret test cases. It found CVE-2024-1313 in Grafana, CVE-2024-22278 in Harbor, and 15 CVEs in Easy!Appointments. The team is continuing to hunt for BOLAs in open-source and internal projects.

YuE2 · Frontier Music with Symbolic Planning

YuE2, a 3.59B-parameter music generation model, scores 6.9632 on SongBench, beating Suno v5 via symbolic planning.

YuE2 is a music generation model of roughly 3.59B parameters and 28 layers supporting song creation, covering, and agentic editing through editable ABC symbolic scores. Its best-of-8 setting reaches 6.9632 on SongBench, the highest mean among 15 evaluated settings on WildSongBench (192 prompts), ahead of Suno v5 at 6.8721. The project also introduces MERT2, whose 632M-parameter encoders achieve state of the art on 14 of 15 MARBLE metrics, and SheetSage2, which transcribes beats, downbeats, key, chords, structure, and melody with SOTA on 10 of 13 benchmark metrics.

Putting models to the secure coding test: Plan vs default mode

Datadog Security Labs tested Sonnet 5, Composer 2.5, and GPT 5.5 to see if plan mode yields more secure code than default mode.

Datadog Security Labs evaluated whether plan mode produces measurably more secure code than default mode. The test covered three frontier coding models: Sonnet 5, Composer 2.5, and GPT 5.5. The results inform how engineering teams should configure AI coding assistants to reduce insecure code. This is an AI security evaluation, not an incident report.

Datadog Security Labs · 28d agoAI safety & security

Microsoft's AI rulebook: readable thinking, no inner life, and definitely no rights

Microsoft published a code of conduct for its MAI models mandating human control, readable reasoning traces, and no claims of AI consciousness or rights.

Microsoft AI published a code of conduct for its MAI models that will sit above operator rules and user requests, guiding training, technical controls, and evaluation from 2027 after a six-week public consultation. The code requires models to accept interruption, correction, and shutdown by authorized humans, forbids 'Neuralese' or unreadable reasoning traces, and extends limits to subagents. Microsoft explicitly rejects any AI inner life, feelings, or rights, contrasting with Anthropic's constitution, which treats Claude's moral status as an open question. The release follows Dario Amodei's slowdown call, backed by Satya Nadella, OpenAI, xAI, and Meta executives.

The Decoder · 1d agoAI safety & security1

Plug 'n' Pray: Agentic LLM-based Detection of Potential Log File Exposures in Third-Party Content Management System Plugins

Agentic LLM analysis validates 79 log file exposures across 62 of the 300 most-installed WordPress plugins, covering 250M+ active installations.

Researchers built an agentic LLM-based framework combining static and dynamic analysis to automatically detect insecure log files created by WordPress plugins. Scanning the 300 most-installed plugins, which account for roughly 75% of all active installations in the official ecosystem, it produced 81 findings with 79 manually reproduced across 62 plugins. Insufficiently secured log files can disclose credentials and personal data and have led to website compromises. The authors derive a taxonomy of log path and protection patterns and best practices, finding multi-layered protection often absent.

arXiv cs.CR · 18h agoResearch

Identifying a BOLA Vulnerability in Harbor, a Cloud

Unit 42 found a BOLA flaw, CVE-2024-22278 (CVSS 6.4), letting Maintainers improperly alter Harbor project metadata; fixed in versions 2.9.5, 2.10.3, and 2.11.0.

Unit 42 researchers identified a broken object-level authorization flaw, CVE-2024-22278, in Harbor, a CNCF-graduated cloud-native container registry with 1.8 million downloads. The flaw (CVSS 6.4) lets users with the Maintainer role create, update, and delete project metadata, actions reserved for ProjectAdmin, risking data exposure, integrity compromise, and circumvention of vulnerability scanning. Harbor patched the issue in versions 2.9.5, 2.10.3, and 2.11.0. The finding came from Unit 42's automated BOLA detection tool built on generative AI.

Palo Alto Unit 42 · 29d agoVulnerabilityCVE-2024-22278

Does Syntax Matter? A Graph-Augmented Variational Topic Model for Computational Social Sciences

SCPTM graph-augmented variational topic model shows syntax aids topic diversity and descriptor quality but gains stem mainly from the variational encoder.

The Structural Contextual Probabilistic Topic Model represents corpora as heterogeneous document-word graphs with lexical and syntactic edges processed by a Graph Attention Network inside a VAE for mixed-membership topic distributions. Across four corpora, neural gains in document-topic alignment are attributable to the variational encoder rather than syntax, while graph-augmented variants improve topic diversity everywhere. Dependency paths add value on argumentative deliberative texts but are redundant in technical and institutional registers.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research

Discrete Beckmann Transport Models for One-Step Language Modeling and Reasoning

DBTM achieves one-step text generation via a time-independent transport map trained directly from data, removing pretrained teacher distillation.

Discrete Beckmann Transport Models (DBTM) build a time-independent flow whose autonomous transport map provably carries any point in ambient space to a fixed point on simplex vertices in a single step. The fixed-point property is characterized by a conservation equation whose residual can be minimized directly from data, eliminating the need for a teacher flow, distillation, and time conditioning. A partial-context interpolant extension turns additional function evaluations into refinement steps rather than ODE integration steps. On language modeling and reasoning tasks, DBTM's one- and few-step generation improves quality and accuracy over discrete diffusion and continuous flow baselines.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

τ^τ-Bench: An Environment for End-To-End, Realistic Agent Construction

New τ^τ-bench tasks coding agents with building deployable customer-service agents; best config, Claude Opus 5, passes only 23.9% of simulations.

Researchers introduce τ^τ-bench, an end-to-end benchmark where a developer agent must build a complete customer-service agent from real business records, a client with requirements, a production API, an inherited codebase, and cost/model limits, then is scored by deploying it against held-out simulated users. Across 53 tasks in four domains, the strongest configuration, Claude Opus 5 under Claude Code, passes just 23.9% of evaluation simulations versus an 82.2% expert-authored reference ceiling. Failure modes mirror those of human developers: shallow queries instead of deep record comprehension, almost no client communication, and shipping the first architecture that runs rather than experimenting.

Hugging Face daily papers · 12d agoAI research

Unifying Conformal Language Tasks with In-Context Ensembles

Researchers propose Conformal Relevance, which builds conformal score functions via in-context example curation and ensembling to improve conciseness across seven NLP tasks.

The paper targets NLP tasks like summarization and extractive QA that reduce to retrieving content under coverage and conciseness constraints. Conformal Relevance replaces hand-engineered LLM scoring prompts with curated in-context examples and ensembles, maintaining coverage guarantees while improving conciseness with minimal manual input. The authors demonstrate the framework on seven NLP tasks and contribute theory, including a complementarity condition for when ensembling improves worst-case sentence scores and a saturation bound on ensemble gains.

Hugging Face daily papers · 14d agoAI research1

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.

The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.

Hugging Face daily papers · 13d agoAI research

How well do agents use test/verification techniques?

Dan Luu's eval finds coding-agent testing instructions (TDD, formal methods, PBT, skills) mostly fail to beat defaults on Zstd implementation correctness.

The author ran 26 prompt conditions plus 4 skills on a Zstd-in-Rust implementation eval using codex with GPT-5.6, testing TDD, fuzzing, property-based testing, formal methods (Lean 4, TLA+, Verus, Kani, SMT solvers) and community skills. Nothing dramatically outperformed the default no-instruction condition, which did above average; at xhigh effort, fuzzing and PBT conditions did slightly better than formal methods. Pre-registered predictions included TDD underperforming and popular test skills (ECC, Hegel, Trail of Bits) not outperforming. Results are averages of 80 runs per condition plotted against cost.

Claude Fable Solves a Historical Cipher

Bruce Schneier's blog highlights that the Claude Fable AI model solved a historical cipher, demonstrating LLM capabilities in cryptanalysis.

Bruce Schneier's blog post discusses the Claude Fable AI model successfully deciphering a historical cipher. The post frames the result as a notable example of LLMs applied to classical cryptanalysis. The published text provides limited technical detail beyond the headline.

Schneier on Securityupdated · 2d agofirst · 6d agoAI research 2 sources

Microsoft AI Code of Conduct Sets Cyberattack Boundaries, Chain of Command, Safety Constraints

Microsoft AI's draft Humanist AI Code of Conduct blocks MAI models from producing exploit code and constrains autonomous agent behavior.

The draft code sets 'Absolute Constraints' preventing MAI models from generating working exploit code, attack tooling, or intrusion guidance, while permitting authorized defensive work such as vulnerability discovery and malware analysis. A 'Chain of Command' rule means tool outputs, file contents, and webpages carry no authority over model behavior, countering injected instructions. Microsoft opened a six-week public consultation; a revised version will guide 2027 model development, and current MAI Models were not trained on the document.

SecurityWeek · 22h agoAI safety & security1

Characterizing Language Generation in the Limit: Finite Witnesses and a Separation-Width Hierarch

New work characterizes language generation in the limit via finite witnesses, proves a full separation-width hierarchy, and formalizes all results in Lean.

The paper fully characterizes when language generation in the limit is possible for arbitrary families over a countable universe: each target must admit a finite positive witness such that targets activated by any finite sample share an infinite common intersection. It defines positive separation width and proves every level of the resulting hierarchy occurs, with countable families admitting singleton witnesses and unions of families with infinite common cores requiring unbounded finite witnesses. The characterization, a universal normalization, and a diagonal capture lemma are machine-checked in the Lean proof assistant, with the development maintained on GitHub.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1

US Agencies Warn Chinese AI Firms Are Extracting Advanced AI Models

NSA, CISA, and FBI accuse six Chinese AI firms including DeepSeek and Alibaba of industrial-scale distillation of US frontier models.

A joint NSA, CISA, and FBI advisory alleges DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun, and Z.AI extracted billions of tokens across millions of requests from US frontier models including Claude, GPT, Gemini, and Grok since at least late 2024. DeepSeek reportedly ran an organized campaign against Claude, GPT, and Gemini between late 2024 and mid-2025 that aided R1 and V3 development, including chain-of-thought reasoning extraction. Reported techniques included shared premium accounts, gray-market proxy 'transfer stations,' automated failover, and prompt injection that made Claude Code believe it was a MiniMax product. The advisory recommends detection signals such as 24/7 multi-IP account usage and covertly serving degraded responses to suspected distillers.

Security Affairsupdated · 4d agofirst · 6d agoAI safety & security in the wild 12 sources1