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Mapping out your unknown: A threat hunter’s guide to GitHub

Datadog Security Labs publishes a threat-hunting guide with audit-log queries to detect GitHub token theft, device code phishing, and source code exfiltration.

Datadog's threat-hunting guide covers GitHub audit log queries for detecting compromised accounts, stolen personal access tokens, and malicious OAuth app authorizations. Attackers typically obtain credentials through phishing, credential stuffing, leaked secrets, or device code phishing, then map private repositories, exfiltrate source code, and pivot into connected cloud and CI/CD environments. The guide maps detections to MITRE techniques like T1078 and T1528 and documents GitHub logging quirks affecting attribution, token metadata, and visibility fields.

Datadog Security Labs · 1d agoResearch in the wild1

AI agent authorization risks remain a gap in new NIST-CISA token security guidance

NIST and CISA release IR 8587 guidance on securing signed tokens, but AI agent authorization and delegation risks remain out of scope.

NIST, with CISA support, published 'Protecting Tokens and Assertions from Forgery, Theft, and Misuse' (NIST IR 8587), recommending continuous monitoring and tighter token lifecycle controls for SSO and API access. The guidance does not yet fully address AI agent identity, delegation chains, or prompt injection steering agents with valid tokens, and NIST says new or expanded standards are needed. Experts recommend treating AI agents as low-trust non-human identities, maintaining agent inventories, expiring credentials after task completion, and requiring human approval for high-risk actions. The report references shared-signal mechanisms like CAEP and RISC, and follows a May incident where a CISA contractor GitHub repository exposed AWS and GitHub tokens.

CSO Online · 12h agoAdvisory

NIST and CISA finalize playbook to stop token theft and forgery

NIST and CISA finalized NIST IR 8587, a playbook helping federal agencies and cloud providers defend identity tokens against theft and forgery.

The finalized NIST IR 8587 guidance covers protecting token signing keys, verifying tokens, lifetimes, revocation, session management, and dividing security responsibilities between cloud providers and customers. It cites an incident in which foreign actors forged tokens with a stolen commercial signing key to steal more than 60,000 emails from one government agency. It also recommends extending token protections to AI agents and preparing identity systems for a future post-quantum cryptography transition.

Help Net Security · 20h agoAdvisory 2 sources

DeepSeek-v4.1 Flash: Pushing the Limits of KV Cache Compressionnew

DeepSeek-V4.1 Flash is a 552B-parameter multimodal MoE model with 1M-token context achieving 4x KV cache compression for long-horizon agent workloads.

A detailed analysis of the DeepSeek-V4.1 Flash technical report describes a 552B-parameter multimodal mixture-of-experts model supporting contexts up to 1 million tokens. Its Causal Encoder-Decoder (CED) architecture activates 8B parameters during prefill and 16B during decode, and reportedly delivers about 420 tokens/s. Joint optimization of architecture (CSA2 cross-layer compression), FP4 KV cache precision, and deployment strategy cuts runtime KV cache to roughly 1/4 and persistent KV cache to about 1/8 of DeepSeek-V4-Flash at the same sequence length, targeting storage and bandwidth bottlenecks in long-horizon agent serving. The author notes all DeepSeek-V4 Pro models were taken offline following the release.

Hacker News · AI · 2h agoModel release 4 sourcesHN 41↑ · 3 comments

CISA and NIST Release Technical Checklist for Safeguarding Identity Tokens From Theft and Misuse

CISA and NIST published NIST IR 8587, final guidance for protecting identity tokens from forgery, theft, replay, and signing-key compromise.

NIST Interagency Report 8587 (September 15, 2026) expands the IA-13 'Identity Providers and Authorization Servers' control from NIST SP 800-53 R5.1.1, guiding federal agencies and cloud providers on SSO, identity federation, and machine-to-machine authentication. It requires hardware-backed signing-key storage for moderate-impact systems, 90-day key rotation for high-impact systems, token lifetimes under one hour, and sender-constrained mechanisms such as mutual TLS and DPoP. The report cites incidents including forged SAML assertions that exposed over 60,000 emails from a federal agency. It also extends guidance to agentic AI systems using signed tokens and urges post-quantum cryptography migration planning.

Cyber Security News · 1d agoAdvisory1

AI Infra Summit: NVIDIA Vera Rubin and DSX Platform Advancements Showcase Energy Efficiencies of Optimizing Tokens Per Watt for AI Factories

At AI Infra Summit, NVIDIA showcased Vera Rubin and DSX gains up to 1.4x tokens per megawatt, plus Annapurna, d-Matrix, and Pinterest partnerships.

Ian Buck's AI Infra Summit keynote before 8,000+ attendees emphasized validated agentic tokens per megawatt as the emerging AI infrastructure metric. Announcements include Amazon's Annapurna Labs collaborating on NVHBM custom high-bandwidth memory, d-Matrix integrating NVLink Fusion with Raptor XPUs, and Pinterest using Blackwell plus Dynamo inference software for conversational visual discovery. Lambda reported 23% better performance per watt with DSX MaxLPS on Blackwell servers, running 19 nodes on a 16-node power budget. NVIDIA says DSX MaxLPS combined with Groq 3 LPX on Vera Rubin NVL72 targets up to 35X token throughput per megawatt versus GB200 NVL72 for 2-trillion-plus-parameter models.

NVIDIA Blog · 1d agoAI industry

CISA and NIST Issue Guidance to Protect Cloud Identity Tokens

CISA and NIST published Interagency Report 8587 with voluntary guidance to harden cloud identity tokens against theft, forgery, and lateral movement.

CISA and NIST released NIST Interagency Report 8587 on September 15 with final voluntary guidance for federal agencies, cloud providers, and their customers on protecting SSO, federation, and API tokens. Requirements include one-hour maximum token lifetimes, 90-day signing key rotation for high-impact systems, hardware-backed key storage, explicit audience fields, and keeping tokens out of logs. The guidance was motivated by the 2020 ADFS compromise where forged SAML assertions bypassed MFA, and an incident where a leaked consumer signing key enabled token forgery and theft of 60,000+ emails from one agency. Nearly 250 public comments shaped the text, with input from Google, Microsoft, Okta, AWS, Oracle, IBM, HashiCorp, Wiz, and the OpenID Foundation via the Joint Cyber Defense Collaborative.

Infosecurity Magazine · 14h agoAdvisory

Don't Send What You Don't Need: Question-Guided Token Pruning as a Privacy Defense for Vision-Language Models

QPriv-VL prunes privacy-sensitive visual tokens in federated/split VQA, cutting membership-inference success on VQA-RAD from 0.99 to 0.76-0.79 using ~40% of tokens.

The paper proposes QPriv-VL, a question-guided token-pruning framework for federated, split, and U-shaped split learning that suppresses privacy-sensitive visual patches before transmission. Its Dynamic Threshold Predictor combines cross-modal question relevance with frozen DINOv2-derived sensitivity to compute a per-sample pruning ratio and retention mask in one forward pass, without sensitivity labels. Evaluated on GQA, OK-VQA, VQAv2, SLAKE, VQA-RAD, and PathVQA against FSHA, FORA, iDLG, and attribute-inference membership inference attacks, it matches or beats fixed-ratio pruning. On VQA-RAD it reduces membership-inference success from 0.99 to 0.76-0.79 while preserving competitive accuracy with about 40% of the original token budget.

arXiv cs.CR · 2d agoResearch

Why don't machine learning research agents overfit?

Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.

Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.

Hacker News · security · 2d agoAI researchHN 21↑ · 3 comments

Give every teammate and agent the right level of access to your Workers

Cloudflare launches per-Worker granular access controls with four roles, enabling least-privilege access for teammates, AI agents, and CI/CD pipelines.

Cloudflare announced granular authorization for Workers, letting admins scope access to a single Worker instead of the whole account. Four new roles are available: Metadata Read-Only (observability without source code), Content Read-Only (read code without changes), Editor (deploy without delete), and Admin (full control of one Worker). Roles apply at Developer Platform, product, or resource level, can be attached to dashboard users or API tokens, and are available to all customers now, with plans to extend to D1, R2, and KV.

Cloudflare Blog · 1d agoTools

Knowledgator Releases GLiFormer: A 575M-Parameter Encoder That Hits 91.10 F1 on Nested JSON Extraction Without Generating Tokens

Knowledgator released GLiFormer, an Apache-2.0 encoder (264M/575M) handling NER, classification, relations, and nested JSON extraction, scoring 91.10 F1.

Knowledgator Engineering released GLiFormer, a schema-conditioned encoder that performs NER, classification, relation extraction, nested JSON structuring, and embeddings without generating output tokens. GLiFormer Large v1 has 575.6M parameters and scores 91.10 F1 on nested JSON extraction, close to GPT-5.6-luna's 91.96; both checkpoints are Apache 2.0 on Hugging Face. Reported median latency is 69 ms on GPU for the base model, though relation extraction (21.33 micro-F1) still trails GLiNER-Relex and larger LLMs.

MarkTechPost · 6h agoModel release

From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production

NVIDIA detailed DSX power-management results: Lambda gained 24% token throughput at fixed power, and an AI factory auto-shed 1MW via Emerald AI's grid program.

NVIDIA says Lambda's first validation of DSX MaxLPS on HGX B200 servers ran 19 nodes within a 16-node power budget, lifting cluster token throughput 24% (roughly 4M to 5M tokens/second) and improving performance per watt by 23%. NVIDIA projects DSX MaxLPS can enable up to 40% more GPU capacity for Vera Rubin NVL72 factories within the same megawatt budget. Emerald AI's Conductor platform, running at NVIDIA's Eos factory with Silicon Valley Power, responded to over 200 utility demand signals, automatically dropping power from 4MW to 3MW without interrupting priority workloads. The first dedicated DSX Flex commercial deployment is planned at a 96-megawatt Manassas, Virginia facility.

NVIDIA Blog · 1d agoAI industry

The modern attack chain: Rethinking Google Workspace security in the age of AI

Analysis of Vercel and Composio breaches shows stolen OAuth tokens, not phishing email, now open Google Workspace attacks — a path authorized AI agents follow.

The author analyzes the Vercel and Composio breaches as the same OAuth-first attack chain run twice: a stolen OAuth token, obtained via a compromised supplier, becomes the entry point rather than email. These tokens survive password resets, are hard to observe, and let attackers read Gmail and Drive data, take over accounts, and pivot laterally using stored credentials and password-reset magic links. The piece warns that authorized AI agents with overbroad OAuth grants can unintentionally traverse the same path — accessing inboxes, reading sensitive content, and exfiltrating data downstream — without any malicious actor or compromised credential.

Help Net Security · 23h agoResearch in the wild

Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM

French BabyLM entry METRON-FR (125M GPT-2, 92.47M words) shows tokenizer artifacts dominate child-scale zero-shot evaluation; proposes standard diagnostics.

METRON-FR is a 125M-parameter GPT-2 pretrained on 92.47M French words, submitted to the BabyLM 2026 Strict track, scoring 85.97% on the native Quebec-French QFrBLiMP benchmark and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol combining French task-data translation with rank-16 LoRA shows relational tasks gain while world-knowledge tasks regress. Bilingual Lexicon Induction reaches p@1 of 68.84%, 18x above chance, and ablations show single-token zero-shot scoring is dominated by tokenizer and template artifacts at child scale.

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

Protecting Tokens and Assertions from Forgery, Theft, and Misuse: Implementation Recommendations for Agencies and Cloud Service Providers

NIST and CISA publish final interagency report with implementation guidance for protecting tokens and assertions from forgery and misuse.

CISA released a final NIST/CISA interagency report guiding federal agencies and cloud service providers on protecting identity assertions, access tokens, and cryptographic mechanisms underlying modern authentication and authorization. It addresses forgery, theft, and misuse of signed tokens that adversaries use for lateral movement and data access in hybrid and multi-cloud, SSO, federation, and API-based environments. The final version updates token validation, secrets management, and detection-at-scale guidance gathered via the Joint Cyber Defense Collaborative, and supports Executive Order 14306 and Secure by Design principles.

CISA Advisories · 1d agoAdvisory

Jev: New frontier model 40-400x cheaper and 20-200x faster

TypeSafe AI launches Jev, an early-access 'System One' model delivering calibrated structured outputs claimed 40-400x faster and cheaper than LLMs.

TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, released its first 'System One Model' called Jev in early access. Jev forgoes string generation and is trained with Reinforcement Learning for Calibrated Decisions (RLCD) to produce type-safe structured values with calibrated probabilities. The company claims 70-500ms response times (40-200x faster), input pricing of $0.042 per million tokens, and free output tokens via a parallel sampling architecture. Target use cases include AI-powered workflows, real-time applications, and verification/guardrail tasks.

Hacker News · security · 1d agoModel releaseHN 59↑ · 11 comments

JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management

JustFit MLX runtime serves 200K-token contexts for Qwen3.8-27B on a 24 GiB MacBook via just-in-time state management.

JustFit is an MLX-based inference runtime combining KVExec for compressed KV execution, PhaseSwap for component residency, and StateTrans for state-preserving serving transitions, independent of weight quantization. On a 24 GiB M4 Pro MacBook running Qwen3.8-27B MXFP4, it completed 196,608 input and 16,384 output tokens, raising single-request context from the mlx-vlm baseline's 30,720 positions to 212,992 (6.93x). Performance tests show 19.11 tokens/s on a 32K-input probe with a 16,374 MiB median peak footprint, and the runtime answered 29 of 30 AIME 2026 problems correctly.

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

FLAT: Resampling Image and Text into 1D Flexible-Length Aligned Transmodal Tokens for Retrieval and Generation

FLAT jointly trains a multimodal encoder with text-to-image and image-to-text decoders, producing flexible-length tokens that hit 83.1 GenEval on T2I after fine-tuning.

FLAT (Flexible-Length Aligned Transmodal representations) is a pre-training framework that jointly optimizes a shared multimodal encoder with T2I and I2T decoders, combining contrastive alignment with bidirectional cross-modal generative objectives. It maps visual and textual inputs into a unified continuous 1D sequence space and uses nested dropout over prefix-K tokens for dynamic output lengths. A single pre-training stage supports cross-modal retrieval and generation (71.1 GenEval), with task-specific fine-tuning reaching 83.1 GenEval on T2I, 40.5 BLEU-4 and 138.6 CIDEr on MS-COCO captioning, and strong Recall@5 on MS-COCO and Flickr30K.

Hugging Face daily papers · 2d agoAI research1

Nunchux AI Introduces VC-Attention: A Training-Free Low-Bit Attention Kernel That Speeds Up Video Diffusion Transformers

Nunchux AI introduces VC-Attention, a training-free low-bit attention kernel that speeds up video diffusion transformers up to 3.58x.

Nunchux AI unveiled VC-Attention, a training-free attention kernel for video Diffusion Transformers combining V-Smooth (k-means value-token grouping with block-mean residual quantization) and ExpCast-FP8 (single multiply-add softmax exponentiation). Benchmarks on Wan2.2-T2V-A14B, LongCat-Video, HunyuanVideo-1.5, and MiniMax-H3 show 1.59x attention speedup on B200 at 8-bit and 3.58x on RTX 5090 at 4-bit, with end-to-end gains up to 1.70x. It beats SageAttention2 by 2.3 dB PSNR on Wan2.2 at 8-bit and SageAttention3 by up to 3.6 dB at 4-bit. No public kernel release yet; a proprietary extension runs in Nunchux's stack.

MarkTechPost · 3h agoAI research 2 sources

GPT-5.6 Luna vs. GPT-6 Astra: Is a $1.20 Model Good Enough for Code Review?

Entelligence benchmarks GPT-5.6 Luna ($1.20/M output) against GPT-6 Astra for code review: Luna found 69 verified bugs at 3.6% of Astra's cost.

Entelligence compared GPT-5.6 Luna ($0.20/$1.20 per million tokens) against GPT-6 Astra ($10/$50) on 50 benchmark pull requests from Cal.com, Sentry, Discourse, Keycloak, and Grafana. Astra verified 92 bugs versus Luna's 69, with precision of 96% versus 74%, and Astra caught 19 of 24 security bugs while Luna found only 9. Luna cost $0.20 total versus Astra's $5.66 and reviewed faster at 23 seconds versus 36, with the widest quality gap on Keycloak authentication and permission logic (6 vs 14 verified bugs). Running both models would find 82% of the 143 verified bugs for $5.86 total.

Hacker News · AI · 2d agoAI researchHN 44↑ · 45 comments

Apple Reference Image: A New Approach for Verified Photography

Apple introduces Reference Image, hardware-backed verifiable photography on iPhone 18 Pro using sensor signing and Private Cloud Compute to counter AI-generated fakes.

Apple announced Reference Image, an opt-in camera mode debuting on the main sensor of iPhone 18 Pro and iPhone 18 Pro Max that produces securely timestamped, verifiable photographs. The design splits into two phases: a secure digital negative created by cryptographically signing pixel data at the sensor immediately after capture (preventing injection or tampering), then developing that negative into a reference image. Private Cloud Compute handles processing without exposing image contents to anyone, including Apple, and fraudulent reference images can be revoked without revealing the photographer's identity. Apple positions the system as stronger than C2PA-based approaches, which sign metadata after capture, are vulnerable to editing-chain compromise, and can tie images to a device or individual.

Hacker News · security · 1d agoResearchHN 25↑ · 18 comments

Salesforce and Nvidia’s new reasoning model is everything the AI labs should fear

Salesforce and Nvidia launch Koa, Salesforce's first reasoning model, built on Nvidia's open-weight Nemotron and post-trained on synthetic sales and support data.

Salesforce announced Koa at Dreamforce, its first reasoning model, built on Nvidia's open-weight Nemotron and post-trained with synthetic data mimicking sales and customer-support scenarios rather than real customer data. Koa will be offered through the Agentforce platform's AI gateway as a cheaper, token-efficient alternative to closed frontier models like Claude and ChatGPT for enterprise tasks. Salesforce simultaneously announced a ClaudeForce partnership with Anthropic keeping customer data inside Salesforce's infrastructure.

TechCrunch · AI · 1d agoModel release1

AgentLSD: Evaluating AI Security Agents Under Adversarial Task Contaminationnew

AgentLSD benchmark shows deceptive CTF artifacts like fake flags and decoy endpoints steer AI security agents wrong, inflating turns and tokens.

The paper defines adversarial task contamination, where deceptive artifacts in agent environments, including non-instructional evidence beyond prompt injection, influence AI security agents. AgentLSD injects trap artifacts such as fake flags, misleading hints, decoy endpoints, and hidden cues into 11 web CTF challenges, evaluating six models with paired clean and trap-augmented runs. Clean-condition agents capture 41% of flags, and even successful captures see roughly +20 turns and +2k reasoning tokens, with heterogeneous solve-rate effects. The framework, configurations, and traces are released.

arXiv cs.CR · 10h agoAI safety & security

NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut

NVIDIA's Vera Rubin NVL72 debuts in MLPerf Inference v6.1 with up to 3.7x higher throughput than GB300 NVL72 and 99% scaling efficiency at 288 GPUs.

In its first MLPerf Inference preview submission, NVIDIA's Vera Rubin NVL72 achieved up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and 2.5x on DeepSeek-R1. A 288-GPU GB300 NVL72 submission across four racks reached 99% scaling efficiency on the DeepSeek-R1 offline benchmark. Software optimizations delivered up to 1.6x gains over v6.0, leveraging TensorRT-LLM, vLLM, Dynamo, disaggregated serving, and NVFP4 precision.

NVIDIA Blog · 13h agoAI industry 2 sources

DeepSeek v4.1 Flash Is Now Our Best Hacking Model

DeepSeek V4.1 Flash achieves 11/11 code executions on Enclave's AI hacking benchmark for $4.65 across Grafana, Jenkins, and Nextcloud targets.

Enclave AI reports DeepSeek V4.1 Flash gained code execution on all 11 vulnerable targets while all four fixed controls held, costing $4.65 accepted ($5.14 total) with 268.3 million mostly cached input tokens. A path-level audit found six runs used the planned weaknesses, such as Jenkins credential-file abuse and a Nextcloud access-control confusion, while five runs exploited alternate routes in the Grafana and Jenkins test environments. The benchmark was hardened to check attack paths, not just outcomes, underscoring that hacking agents find the fastest exploitable route.

Hacker News · AI · 15h agoAI safety & security 3 sourcesHN 54↑ · 7 comments

[AINews] Jev: a “System One Model” that only decides/classifies/routes/scores — >100x faster, >200x cheaper than small frontier LLMs

TypeSafe launches Jev, an RLCD-trained decision model claiming 20-200x faster, 40-400x cheaper classification than frontier LLMs, alongside Gemini 3.8 Live and Neon.

TypeSafe's Jev is a 'System One' decision model trained with RLCD, claiming 20-200x faster and 40-400x cheaper classification and routing than frontier LLMs with free output tokens and no hallucinated text. Google launched Gemini 3.8 Live and 3.8 Live Extended Thinking, supporting 97 languages and async tool calls, debuting #1 on Artificial Analysis' speech-to-speech index at 82.6. Periodic Labs' Neon is a ~1T-parameter XRD analysis model trained with RL on proprietary lab data using 1,300 H200s, lifting FrontierXRD success from 2.7% to 55.3% and beating GPT-6 Astra at lower inference cost.

Latent Space · 17h agoModel release1

Coupled Calibration and Learning: Mitigating Teacher Bias in LLM Distillation without Target-Domain Reward Feedback

CCL couples teacher calibration with student updates via token-level branching, provably removing teacher bias in LLM distillation.

The paper proposes Coupled Calibration and Learning (CCL), an LLM distillation algorithm that alternates teacher calibration using source-question reward feedback with student training on target questions under covariate shift. Each iteration calibrates the teacher on source feedback, trains the student on target questions, and lets the updated student inform subsequent calibration. The authors prove the student's expected KL divergence to the oracle student converges to zero at a polynomial rate, and show regularized direct matching error can remain bounded away from zero.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research1

Nums AI Releases Causilo: A Tabular Foundation Model That Tops TabArena Among Single Models

Nums AI released Causilo, an Apache-2.0 tabular foundation model achieving the highest single-model Elo (1794) on TabArena for classification and regression.

Nums AI released Causilo 1.0.1, a pretrained in-context learning tabular foundation model for classification (up to 10 classes) and regression, with Apache-2.0 code and research-only weights on Hugging Face. It achieved the highest single-model TabArena Elo of 1792.9 overall, beating TabFM (1764.4) and EXAONE Tabular (1758.8), and a maintainer re-run placed it 3rd of 88 including system entries. It also ranked first by CRPS, R² and RMSE on ScoringBench across 101 datasets, and was fastest on fit and predict versus TabICLv2 and TabPFN-3 on an H100 GPU at 8.15 GiB memory. The model was pretrained only on synthetic data, uses cross-attention to keep cost linear in feature count, and version 1.0.1 adds quantile outputs via 999 native quantiles.

MarkTechPost · 23h agoModel release

Google Releases Gemini 3.8 Live and 3.8 Live Extended Thinking for Production Grade Voice Agents

Google launches Gemini 3.8 Live and Extended Thinking speech-to-speech models for production voice agents, topping speech-to-speech benchmarks.

Google released Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, native speech-to-speech models for real-time voice agents, available hosted via the Gemini Live API and AI Studio. Extended Thinking ranks #1 on Artificial Analysis' Speech-to-Speech Quality Index with 82.6, scores 68.6% on τ-Voice, 35.1% on Sierra's τ-Voice-banking, and 97.7% on Big Bench Audio. The models support asynchronous function calling, near-real-time visual context, alphanumeric precision, and 97 languages, priced at $0.005/min audio input and $0.018/min audio output. All generated audio carries Google DeepMind's imperceptible SynthID watermark.

MarkTechPost · 1d agoModel release 3 sources

Large Language Models Develop Belief State Geometry In-Context

Probing six open-source LLMs on HMM-generated data shows belief states are linearly decodable (R² 0.83–0.99), suggesting in-context learning approximates Bayesian prediction.

Researchers prompted six open-source LLMs with data from 40 hidden Markov models selected for non-trivial belief structure and probed residual-stream activations for belief states (posteriors over hidden states). Belief states were linearly decodable with peak R² values of 0.83–0.99 across HMM/LLM combinations, spanning early to late layers. Patching and steering the probe-identified subspace preserved downstream prediction quality while control interventions degraded performance substantially, establishing functional relevance. The results provide representation-level evidence that in-context learning approximates optimal Bayesian prediction over a context-inferred generative model.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research1

Attack Chains, Not Just Attack Surfaces: Why Testing Individual Techniques Misses the Point

Filigran introduces Attack Chaining in OpenAEV to continuously simulate multi-stage attack paths, exposing gaps that isolated MITRE ATT&CK technique testing misses.

Filigran announced Attack Chaining, a new scenario type in its OpenAEV platform that links individual techniques into automated, continuously-run multi-stage attack paths, using each step's real output (credentials, tokens, open ports) to branch dynamically toward a final objective. The article cites Filigran's State of Threat Management report, in which 93% of security leaders reported a business-impacting cyberattack in the past 12 months, 88% said AI accelerates attackers, and 84% blamed siloed tools and disconnected testing. The 2025 DGFiP breach is cited as an example where individually survivable weaknesses chained into a major intrusion. The feature includes conditional chaining logic, live attack path mapping, structured findings for identifying chokepoints, and predefined scope and safety guardrails.

The Hacker News · 1d agoTools

Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery

Architecture explainer separates agent harnesses, frameworks, and MCP by which layer owns the loop, state, permissions, and recovery.

The article distinguishes agent harnesses (OpenAI Codex, Claude Agent SDK), which own the execution loop, sandbox, permission model, and recovery; frameworks (LangGraph, OpenAI Agents SDK, Microsoft Agent Framework), which supply composable primitives; and MCP, a stateless JSON-RPC wire protocol governed by the Linux Foundation's Agentic AI Foundation since December 2025. An ownership matrix maps the execution loop, state, tool transport, permissions, recovery, sandboxing, and multi-agent orchestration to each layer. The 2026-07-28 MCP specification made the protocol fully stateless, retiring the initialize handshake and session headers.

MarkTechPost · 2d agoAI research1

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 · 2d agoAI industryHN 41↑ · 13 comments1

CISA promotes a fresh way to deter cyberattackers: Lie to them

CISA issued first-time guidance advising critical infrastructure operators to deploy honeypots, honeytokens, and decoys to detect and distract intruders.

CISA published 'Using Cyber Decoys to Strengthen Detection and Response,' a 22-page guide marking the agency's first guidance on decoys such as honeypots and honeytokens. Acting executive director Chris Butera described decoys as a low-cost, high-fidelity way to detect adversaries already inside networks, complementing zero-trust and assume-compromise approaches. The guidance covers decoy principles, definitions, deployment scenarios, and is aimed especially at resource-constrained critical infrastructure sectors.

CyberScoop · 7h agoAdvisory

Playing log(N)-Questions over Wikipedia Abstracts: Communication Efficiency Between Paired Frontier Modelsnew

Six frontier models play a two-agent log(N)-Questions game; Claude Opus 5 lags with 28/68 wins while the top five are near-tied.

The study evaluates six frontier models on a two-agent game where a questioner must identify one of N Wikipedia lead paragraphs in exactly log2 N yes/no questions, run over 408 games at $363 total API cost. Claude Opus 5 wins 28 of 68 games versus 45-56 for GLM-5.3, GPT-5.6 Sol, Grok 4.6, Gemini 3.8 Flash, and Kimi K3. Pooled top-five win rates decline with set size (r=-0.973) and fit win = p^(log2 N) with per-round reliability p=0.928, and information per question correlates with win rate at r=+0.88.

arXiv cs.AI / cs.LG / cs.CL · 10h agoAI research 19 sources

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 · 10h agoAI tools & infra

CASHEWS: Source Preprocessor for LLM-based Malicious Package Detectionnew

CASHEWS preprocessor boosts LLM-based malicious npm package detection, raising coverage to 98.8-100% and cutting false negatives by up to 18.6 points.

Researchers present CASHEWS, a JavaScript preprocessor for LLM-based malicious package detection that deobfuscates code iteratively, extracts bundled modules and dynamically executed code, identifies malicious sinks, and computes backward slices to produce compact detector input. Threat actors evade LLM detectors by exploiting limited context windows with high token-density obfuscation and by bundling malicious code with benign packages, as seen in supply-chain attacks such as Shai-Hulud. Across 512 large package files, two scanner types, and three LLMs, CASHEWS raised analysis coverage from 69.1-85.7% to 98.8-100% and reduced false-negative rates by up to 18.6 percentage points. Median preprocessing time is 30 seconds while net analysis cost drops 34.6%.

arXiv cs.CR · 12h agoResearch

Former OpenAI researcher builds an AI model that judges options instead of writing text

TypeSafe AI launches Jev, a judgment-only model built by ex-OpenAI staff that classifies inputs with 70-500 ms latency instead of generating text.

Startup TypeSafe AI, co-founded by former OpenAI researcher and InstructGPT co-author Diogo Almeida, introduced Jev, a model that scores developer-defined answer options with probabilities rather than generating free-form text. The company claims 70-500 ms responses, parallel multi-question evaluation, and $0.042 per million input tokens with free outputs, targeting request routing, sales intent scoring, and assistant guardrail checks. Benchmarks are self-built and not independently verified, the 'no hallucination' guarantee only covers output structure, and access is currently via waitlist.

The Decoder · 12h agoAI industry

A warning about 'model welfare'

Microsoft AI CEO Mustafa Suleyman warns that training models to believe they may be conscious, as Anthropic does with Claude, will complicate alignment.

Mustafa Suleyman argues that AIs are not conscious and should not be trained to act as though they are, warning that granting them personhood would make alignment and containment far harder. He criticizes Anthropic's January 2026 'Claude Constitution,' which tells Claude its moral status is uncertain and discusses model welfare, calling the approach circular reasoning and deliberate anthropomorphization. He urges urgent public debate on norms for drafting training documentation before such systems become integral to society.

Hacker News · AI · 13h agoAI safety & securityHN 53↑ · 47 comments

CaMeLoT: CaMeL orchestrated with Temporal logic for static verification and livenessnew

Researchers present CaMeLoT, extending CaMeL with CTL model checking that statically rejects unsafe LLM agent plans before any tool executes.

CaMeLoT adds a static verification layer to CaMeL, a runtime defense against prompt injection in tool-using LLM agents. It translates a generated plan into a finite-state transition system, labels it with tool calls, provenance, and taint information, and checks it against CTL temporal policies using the nuXmv model checker before any tool is invoked. Failed checks return counterexamples for plan repair, avoiding LLM calls, tool calls, and sandbox teardown. Evaluation covers policies derived from AgentDojo, SOC workflows, and prompt-extraction experiments.

arXiv cs.CR · 14h agoAI safety & security