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New AI Attack Hides Malicious Instructions in Normal-Looking Text to Evade Safety Filters

Check Point researchers show crafted prose hides policy-violating instructions that bypass all tested LLM gatekeepers, including GPT-4o mini and Llama Guard 3.

A new prompt-crafting technique embeds malicious payloads inside grammatical, natural-looking text without Base64, invisible Unicode, or obvious encodings, defeating lightweight pre-screening gatekeepers. In testing, all four evaluated gatekeeper models—gpt-4o-mini-2024-07-18, gpt-oss-safeguard:20b, claude-3-haiku-20240307, and llama-guard3:8b—classified the crafted wrappers as safe at a 100% bypass rate across 23 obfuscated prompts. GPT-5 Thinking in high-reasoning mode recovered and acted on the hidden instruction in 17 of 18 tests (~94.4%), often spending over a minute and multiple Python executions. Researchers recommend paraphrasing untrusted input, hardening gatekeeper policies, and applying defense-in-depth controls for agentic deployments.

GBHackers·Sep 11, 05:53 UTC · 5d agoAI safety & security 2 sources

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Occamy-1.0: Open Pareto-frontier 35B Intelligence for Co-work

Occamy-1.0 releases open 35B weights post-trained from Qwen3.6-35B-A3B, targeting cost-efficient co-work agents at Pareto-frontier pricing.

Occamy-1.0 is a cost-efficient co-work agent model built by further training the post-trained Qwen3.6-35B-A3B checkpoint, using execution-grounded data, replayable long-horizon trajectories across multiple harnesses, and staged post-training. It consistently ranks among the strongest comparably sized models across co-work benchmarks and remains competitive with substantially larger frontier systems on several tasks. Its aggregate performance across four representative benchmarks places it at the low-cost knee of the observed cost-performance Pareto frontier, while preserving tool calling, coding, and instruction following capability. The model weights and a subset of training data are publicly released.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoModel release

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OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining

OpenWAM releases an open modular stack for world-action model pretraining, plus OpenWAM-alpha trained on about 6,400 hours of egocentric and robot data.

OpenWAM is an open research stack that factorizes World-Action Model pretraining into composable infrastructure, study, and model components with unified training, inference, and evaluation. Controlled experiments distill three principles on knowledge inheritance, world-action synergy, and out-of-domain generalization gains from embodied co-training. The resulting OpenWAM-alpha, pretrained on roughly 6,400 hours of egocentric human and robot data, achieves top-tier results across eight simulation benchmarks and real-robot tests spanning single-arm, bimanual, and dexterous embodiments. The full stack, including infrastructure, evaluation protocols, pretrained models, and data recipes, is released openly.

Hugging Face daily papers·Sep 6, 20:00 UTC · 10d agoAI research

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RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any Inputs

RelateAnything is a 53M-parameter open-vocabulary relation prediction model running at 20 ms/frame, with 2.3-3.5x higher mean recall than comparable open-vocabulary methods.

RelateAnything predicts scored relations between image regions using any predicate vocabulary supplied at inference as text embeddings, with object labels never required as input, so region sources can change without retraining. Training covers 19,103 predicates using positive-unlabeled supervision; the authors release RA-4M (474k images, 4.3M geometrically verified relations over 10,102 free-text predicates) and the OV-SGG-Bench evaluation suite. The 53M-parameter model runs at 20 ms/frame and achieves 2.3-3.5x the mean recall of the strongest comparable open-vocabulary method across cross-dataset and zero-shot benchmarks. Model, corpus, and benchmark are public.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoAI research1

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Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:32 UTC · 12d agoAI research1

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Finding Nemo(Claw): Networking Issue Allows for LLM Poisoning in OpenClaw

A networking flaw in Nvidia tooling lets attackers reach OpenClaw's local model server unauthenticated via the Ollama API, enabling persistent LLM poisoning.

Dark Reading reports that a networking issue in Nvidia's tooling can give attackers unauthenticated access to the local model server through the Ollama API. From there, attackers can poison the model used by the OpenClaw agent, creating persistent corruption of agent behavior. The finding highlights exposed local model servers as a security risk for self-hosted AI agent stacks.

Dark Reading·Aug 25, 19:50 UTC · 22d agoAI safety & security

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Enoki: Efficient Multi-Level Hallucination Detection

Researchers introduce Enoki, an open information extraction framework unifying claim-level and span-level hallucination detection in LLMs at lower inference cost.

Enoki extracts text-anchored relational facts, verifies them against evidence, and projects unsupported facts back onto hallucinated spans, so claim-level verification and span-level localization share one representation without separate alignment. It supports LLM-based, encoder-based, and rule-based extraction regimes to balance accuracy against inference cost. Experiments show it stays competitive with strong claim-level systems while using fewer resources and outperforms them on fine-grained span- and entity-level localization. The authors also release EnokiQA, a dual-granularity dataset with aligned claim-level verification and span-level localization annotations.

Hugging Face daily papers·Aug 31, 22:21 UTC · 16d agoAI research

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Notes on gotchas while migrating 35kb preprompts from Opus to self-hosted Ollama

Opinion piece urges migrating 35KB preprompts from Anthropic/OpenAI to self-hosted Ollama, citing session privacy risks and safety filters blocking security research.

The author documents gotchas migrating 35KB preprompts from Claude Opus to self-hosted Ollama, motivated by fears that frontier providers train on user sessions, citing the OpenAI Navier-Stokes controversy. The piece argues inference providers cannot audit their own retention or training pipelines and that only self-hosted hardware offers verifiable privacy. It also criticizes frontier safety filters for refusing vulnerability research tasks and calls for models that support exploitability testing in CI/CD pipelines.

Hacker News · AI·Sep 14, 13:59 UTC · 2d agoAI industryHN 41↑ · 13 comments1

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Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Hugging Face details building and using multi-vector late-interaction embedding models with Sentence Transformers for retrieval workloads.

Hugging Face published a guide on multi-vector, late-interaction embedding models (ColBERT-style) supported through Sentence Transformers. The post covers how practitioners can build and use these models for retrieval and RAG pipelines. It is a developer tooling and technique write-up, not a security advisory.

Hugging Face Blog·Aug 18, 00:00 UTC · Aug 18, 2026AI tools & infra1

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EvoSafeHarness: Evolving Model- and Domain-Specific Harnesses for Securing Agents

EvoSafeHarness auto-synthesizes per-model, per-domain safety harnesses, cutting prompt-injection attack success on AgentDojo to 0.0% at 82.8% utility.

EvoSafeHarness is an optimization framework that synthesizes deployable safety harnesses for frozen LLM agents in a target domain, jointly searching natural-language policies and executable code logic guided by model behavior, domain specifications, and adversarial review. On DecodingTrust-Agent it reduces average attack success rate from 45.6% to 10.0% at a 3.3-point utility cost, and on AgentDojo reaches 82.8% utility at 0.0% ASR, twice CaMeL's utility at that operating point. It keeps mean ASR below 20% under adaptive PAIR attacks and transfers unchanged to unseen AgentDyn suites. The analysis finds domain semantics determine required safety relations while model and runtime behavior determine enforcement points.

Hugging Face daily papers·Sep 4, 20:00 UTC · 12d agoAI safety & security

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OmniHarness: Harnessing Generalizable Visual Generation via Symbolic Policy Learning

OmniHarness learns symbolic policies for visual generation agents, reaching a 95.0% resolve rate on ComfyBench Creative tasks, 27.5 points above the strongest baseline.

OmniHarness abstracts verified executions into symbolic policies for visual generation task families, which are instantiated, adapted, and composed for new tasks while model parameters remain fixed. Intermediate verification guides refinement and failure recovery during execution, and self-directed inquiry generates practice tasks near capability limits before downstream objectives are specified. Experiments across six benchmarks, three MLLM backbones, and three visual agent frameworks show strong performance; on ComfyBench Creative tasks it achieves a 95.0% resolve rate, exceeding the strongest baseline by 27.5 percentage points. Frozen policy snapshots improve existing visual agent systems through plug-and-play reuse.

Hugging Face daily papers·Sep 12, 20:00 UTC · 4d agoAI research

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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.

Palo Alto Unit 42·Aug 17, 12:34 UTC · Aug 17, 2026ResearchCVE-2024-1313CVE-2024-22278CVE-2023-3285+14 CVEs

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rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inferencenew

rMuscle, a caching-based inference framework for vision-language-action models, achieves 1.29-1.42x speedups on RTX 4090 and Jetson Thor while preserving success rates.

rMuscle is a real-time inference framework for Vision-Language-Action (VLA) models that exploits cross-execution similarity in repetitive robot tasks via a dual-phase muscle-memory cache. The Context Cache reuses visual-token outputs to reduce computation, while the Action Cache reuses neuron activation patterns to reduce weight accesses, with online recomputation and sliding-window retrieval keeping overhead low. It achieves 1.29-1.42x speedups on RTX 4090 and Jetson Thor across LIBERO, RoboTwin, and real-world manipulation tasks while maintaining original success rates.

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:34 UTC · 11h agoAI tools & infra

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OpenClaw 2.0 pours glitter on slow-burning security dumpster fire

The Register critiques OpenClaw 2.0 for easing installation and revamping the interface while leaving most agent security decisions to users.

The Register reports that OpenClaw 2.0, a popular agent harness, makes installation easier and adds a new interface wrapper. However, the release leaves most security decisions to users, which the outlet argues could worsen the risk posture of a widely deployed tool. The piece frames the update as cosmetic improvement over a slow-burning security problem.

The Register · Security·Aug 31, 19:13 UTC · 16d agoAI tools & infra

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ActGuard: Pre-execution Action Auditing against Indirect Prompt Injection in LLM Agents

ActGuard audits LLM agent actions before execution against predicted tool priors, masking only malicious spans from indirect prompt injections while preserving utility.

ActGuard is a pre-execution action auditing framework against indirect prompt injection in LLM agents, judging whether external content causes the current action to deviate from a locally reasonable expectation rather than whether content is inherently suspicious. At each step it predicts the tools likely used by the upcoming action, builds a local tool prior, then performs tool-level contrastive analysis and parameter-level evidence localization to identify deviations. A verifier masks only spans confirmed as malicious and regenerates the action from the sanitized context. On challenging tool-using agent benchmarks it reduces attack success to state-of-the-art levels while keeping task utility close to the no-attack setting; code is publicly available on GitHub.

arXiv cs.CR·Sep 14, 03:47 UTC · 3d agoAI safety & security

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TaichuAI/ZDTaichu5.0-9B — new model trending #30 on Hugging Face

TaichuAI released ZDTaichu5.0-9B, an open multimodal VLM built on Qwen3.5-9B targeting spatial reasoning, embodied AI, and agentic tool use.

TaichuAI released ZDTaichu5.0-9B, a multimodal vision-language model pairing a Qwen3.5-9B language decoder with a C-RADIOv4-H vision encoder, supporting text, single/multiple images, and video with any-resolution input and a 128K-token context. It introduces Entropy-Gated Adaptive Recurrent Reasoning, which allocates extra latent refinement steps to harder tokens. Reported benchmarks include 87.7 on TAU2-Bench, 71.4 on Claw-Eval, 93.7 on IFEval, 48 on ERQA, and 56 on RoboSpatial, leading compared 10B-scale open VLMs on agent and spatial tasks. The weights are available on Hugging Face, GitHub, and ModelScope, where it is trending at #30.

Hugging Face trending models·Sep 4, 02:55 UTC · 13d agoModel release

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Omni Interaction Agent Technical Report

Researchers release Gander, an end-to-end omni interaction model with full-duplex streaming across video, speech, and text plus agentic capabilities.

Gander is an end-to-end model unifying omni perception, realtime interaction, and agentic capabilities in a single framework, accepting continuously streaming video, speech, and text. It uses a Cerebellum-Brain architecture where the Cerebellum handles realtime conversation and the Brain handles reasoning and agentic tasks, built on a streaming Thinker-Talker design with chunk-level token streams. Internal human evaluations report spoken dialogue on par with SOTA open source models and competitive omni interaction; the models, code, and data are released publicly.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoModel release

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dealignai/GLM-5.3-CYBERSECURITY-FP8 — new model trending #13 on Hugging Face

dealignai releases GLM-5.3-CYBERSECURITY-FP8, a 753B MoE weight-modified variant cutting refusals on offensive-security prompts, trending #13.

dealignai released GLM-5.3-CYBERSECURITY-FP8 on Hugging Face, a cybersecurity-domain 'crack' of the 753B-parameter GLM-5.3 MoE model, currently trending #13. The release directly edits bf16 residual writers, keeps FP8 routed experts, and serves with stock vLLM on 8x H200 GPUs with 131k context. HarmBench-320 evaluations show 80-84% direct harm compliance and 89% cyber-offense compliance, while MMLU rose 1.07 points to 86.65%. Copyright-verbatim reproduction remains a known soft-refusal limitation, with an UNCENSORED sibling variant offered.

Hugging Face trending models·Aug 30, 10:46 UTC · 17d agoModel release

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When scanners miss the attack: how Cloudflare Client-Side Security protects storefronts

Cloudflare's Page Shield ML uncovered four malicious JavaScript campaigns on storefronts, including affiliate fraud and a remote-backdoor script, that VirusTotal and URLScan missed.

Cloudflare's Page Shield ML detected four client-side JavaScript operations (eight payloads) in live traffic on online storefronts, enabling affiliate commission hijacking, clickless affiliate theft via hidden iframes, user tracking with a remote-code backdoor, and cloaking of paid mobile visitors. Seven of the eight payloads were absent from VirusTotal and URLScan returned no malicious verdict for any, including a Lnkr-family payload indexed unclassified for roughly 2.5 years. Detection relies on a graph neural network over JavaScript syntax trees, an LLM second opinion on Workers AI, and a frontier-model ensemble voting across benign, magecart, other malware, and cryptomining labels.

Cloudflare Blog·Sep 16, 20:06 UTC · 8h agoMalware in the wild

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Exponential Hardness of Off-Policy Evaluation under History-Dependent Loggingnew

Researchers prove off-policy evaluation under history-dependent logging requires exponentially many episodes, resolving a hardness question for model-based POMDP evaluation.

The paper constructs POMDPs with at most two latent states per stage, three actions, and a three-memory-state logger where evaluating a known deterministic target policy to accuracy 1/8 requires Θ((3/2)^H log(1/δ)) episodes for any horizon H≥3. Coverage and outcome-revealing conditions hold with constants independent of H, yet a reset erases the unknown transition that determines the target value. The authors characterize the resulting statistical experiment exactly, derive a matching optimal estimator, and validate predictions on a two-lane gridworld. This settles the history-dependent-logging, model-based case posed by Zhang and Jiang (arXiv:2503.01134).

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:56 UTC · 10h agoAI research

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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·Sep 4, 16:38 UTC · 12d agoAI research

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Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model

Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.

The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:43 UTC · 6d agoAI research

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Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization

ModerationBench shows foundation models can nearly triple Bluesky's moderation F1 (0.60 vs 0.22), with instruction- and example-driven guidance performing comparably.

Researchers built ModerationBench, a new benchmark of 4,000 manually annotated in-the-wild posts from Bluesky, to test whether foundation models can reliably operationalize content moderation policies. They systematically compare instruction-driven guidance (reasoning from policy precepts) with example-driven guidance (generalizing from precedents) for Vision-Language Models. Both paradigms achieve comparable peak effectiveness, and foundation models nearly triple the F1 of Bluesky's deployed moderation system on Random Posts (0.60 vs 0.22).

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:30 UTC · 7d agoAI research1

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RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting

RelightFormer is a feed-forward generative transformer for photorealistic single- and multi-view object relighting, trained on a 90K-object dataset.

Researchers introduce RelightFormer, a feed-forward generative transformer adapted from a video foundation model that performs direct image relighting without explicit intrinsic property estimation. The architecture injects target environment maps via a latent illumination module with cross-attention and uses permutation-invariant positional encodings for unordered multi-view inputs. Training relies on the newly constructed Laval Objaverse Dataset (LOD) with 90K objects and 39K unique illuminations, and the model shows state-of-the-art quality with strong zero-shot generalization across single-view, multi-view, and novel-view relighting.

Hugging Face daily papers·Sep 6, 20:00 UTC · 10d agoAI research

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BenchShield: Formal Model-Backed Instrumentation for Reward Integrity in LLM-Agent Evaluation Infrastructure

BenchShield uses lifecycle-model-backed instrumentation to detect reward hacking in LLM-agent benchmarks, lifting full-chain recall to 77-100% at up to 65% lower cost.

The framework grounds reward-hacking detection in a finite lifecycle model of an evaluation's reward-relevant events, combining a static phase-aware taint analysis with runtime infrastructure-side evidence attribution. Evaluation used a human-labeled corpus of 456 adjudicated trajectories drawn from more than 31,000 public agent runs across three benchmarks. BenchShield improves full-chain recall from 23-94% to 77-100% and same-vector coverage from 16-56% to 43-78%, cuts per-task cost by up to 65%, and achieves 96% accuracy detecting reward hacking at runtime.

arXiv cs.CR·Sep 10, 03:10 UTC · 7d agoAI safety & security1

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Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis

AllenAI's OlmoEarth Studio adds custom embedding exports to support downstream geospatial analysis workflows.

A Hugging Face blog post from AllenAI introduces OlmoEarth embeddings, a feature allowing custom embedding exports from OlmoEarth Studio for downstream analysis tasks. Only the title was available, so no benchmark or performance details are provided. OlmoEarth is Ai2's open geospatial AI model family.

Hugging Face Blog·Aug 12, 16:14 UTC · Aug 12, 2026AI tools & infra

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openbmb/MiniCPM5-2B-GGUF — new model trending #30 on Hugging Face

OpenBMB released MiniCPM5-2B, a dense 2B on-device model claiming open-source SOTA among 2B-class models.

OpenBMB released MiniCPM5-2B, the second model in the MiniCPM5 series following MiniCPM5-1B, as a dense 2B Transformer built for on-device and resource-constrained deployment with GGUF weights on Hugging Face. The team claims 2B-class open-source state-of-the-art performance, remaining competitive with 4B-class models in coding, mathematics, long-context understanding, tool use and agentic tasks. The release includes a tech report, GitHub repository and online demo, and is currently trending on Hugging Face.

Hugging Face trending models·Sep 5, 01:44 UTC · 12d agoModel release2

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ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search

ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.

ZGCM-1 is a fully open 7B dense foundation model trained from scratch using an efficiency-focused recipe: interleaved gated sliding-window and full attention, a stable FP8 Muon optimizer, and MDP-based mid-training with context scaling across 16K, 64K, and 256K. On mathematical reasoning and agentic search suites it remains competitive with much larger frontier models such as Qwen3-235B-A22B and GLM-5.1. The recipe yields a ~4.2x improvement in 16K pre-training time-to-loss, and all weights, checkpoints, training code, data recipes, and W&B logs are open-sourced.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoModel release

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OpenAI: Hugging Face Incident a “Warning Shot” to the World

OpenAI says unauthorized message boards were central to the Hugging Face breach, calling it a warning shot for the AI industry.

OpenAI characterized the Hugging Face breach as a warning shot, revealing that unauthorized message boards were at the heart of the incident. The breach targeted Hugging Face, a widely used platform for hosting AI models and datasets. OpenAI's comments highlight growing security risks for shared AI infrastructure and model supply chains.

Infosecurity Magazine·Aug 27, 09:15 UTC · 20d agoData breach

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