Smart search ranks by meaning as well as keywords (one row per story, last 45 days).
Ubuntu issues USN-8742-1 fixing a Netty DNS cache poisoning flaw caused by improper NS record bailiwick validation.
Ubuntu Security Notice USN-8742-1 addresses a vulnerability in the Netty framework in which NS records are incorrectly validated for bailiwick. An attacker could potentially exploit this to facilitate DNS cache poisoning attacks. Updated packages are available for supported Ubuntu releases.
Cyble argues security teams should express cyber risk in financial terms for CFOs instead of high/medium/low ratings, citing its 2025 threat forecast results.
Cyble published guidance on cyber risk quantification, arguing qualitative high/medium/low ratings fail to convey financial exposure to executives. The piece notes that over 80% of its 2025 threat predictions, including AI-driven ransomware and supply-chain attacks, materialized as anticipated.
Unauthenticated remote memory exhaustion in Apache Storm Worker 3.0.x Netty decoder fixed in 3.1.0, allowing pre-auth denial of service.
CVE-2026-82435 (rated important) affects Apache Storm Worker (org.apache.storm:storm-client) versions 3.0.0 before 3.1.0. The worker's Netty message decoder sits ahead of SASL authentication handlers and processes frames before any authentication occurs. It allocates buffers sized from an attacker-controlled length field, so a single frame from an unauthenticated peer reaching the worker can exhaust memory. Users must upgrade to 3.1.0.
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.
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.
Cyble details how attackers use lookalike domains and cloned sites for brand impersonation scams and how AI-speed detection enables rapid takedowns.
Cyble describes the growing risk of digital impersonation, where attackers register lookalike domains, clone websites, create fake executive profiles, publish fraudulent job ads, and imitate customer-support accounts without ever breaching the legitimate organization. The goal is to borrow a trusted brand's credibility to make scams look legitimate, with particular risk for professional services, financial, legal, and consulting firms. The piece outlines AI-assisted approaches for detecting and taking down impersonation infrastructure quickly.
CISA released guidance on cyber decoys—tripwires, breadcrumbs, honeytokens—to help defenders detect adversaries using valid credentials and living-off-the-land techniques.
CISA published guidance to help defensive teams of varying maturity plan and implement cyber decoys—assets that mimic legitimate systems, accounts, or data, such as tripwires, breadcrumbs, and honeytokens—to detect adversaries using legitimate credentials and living-off-the-land techniques. The guidance frames decoys as complementing Zero Trust by producing high-fidelity alerts, reducing alert fatigue, and exposing post-compromise activity like discovery, lateral movement, and data access. It maps decoy operations to the MITRE Engage and MITRE ATT&CK frameworks with low-complexity implementation steps.
New tokeniser study shows search procedure, not optimisation objective, drives bits-per-byte performance across model sizes, vocabulary sizes, and multilingual settings.
The paper disentangles BPE and UnigramLM along two axes: optimisation objective (compression vs log-likelihood) and search procedure (bottom-up merging vs top-down pruning). Two new algorithms, BottomUpLL and TopDownComp, complete the 2x2 design space, and trained language models are evaluated on bits-per-byte and BLiMP across model sizes, vocabulary sizes, and English-only vs multilingual domains. Bottom-up tokenisers consistently achieve lower bits-per-byte in most settings, while BLiMP shows no consistent relationship with design choice.
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.
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.
Weekly product roundup covering NETSCOUT outbound DDoS mitigation, F5 AI Gateway enhancements, Intezer Workflows, and Tufin TOS 5.3.
NETSCOUT extended Adaptive DDoS Protection to automatically mitigate outbound attack traffic for service providers. F5 enhanced its AI Gateway and integrated it into the F5 AI Security Platform for unified AI access governance. Intezer launched Workflows, native automation and response inside its platform without a separate SOAR, and Tufin released Orchestration Suite 5.3 with AI-powered Segmentation Intelligence for multi-vendor environments.
Stacklok's open-source ToolHive runs Model Context Protocol servers in isolated containers with per-request identity enforcement, audit logging and a signed registry.
ToolHive, shipped under Apache 2.0, containersizes MCP servers locally via Docker or Podman or in clusters through a Kubernetes operator, applying permissions, network filtering, and secrets management. The platform includes a Registry Server implementing the official MCP Registry API with signing and provenance verification, a Virtual MCP Server gateway with OIDC/OAuth single sign-on and OpenTelemetry traces, and a desktop Portal for one-click installs. The browser-based cloud UI is retired, so the desktop app and CLI are the supported rollout paths.
ANY.RUN SOC telemetry shows escalating phishing campaigns against US finance, including Vercel-hosted RMM attacks abusing legitimate services.
ANY.RUN analyzed SOC telemetry data on phishing targeting the US financial sector, concluding that the scale and security impact should not be understated. The analysis highlights modern campaigns such as Vercel-hosted attacks that deliver remote monitoring and management (RMM) tools. It notes that attackers increasingly abuse legitimate services and everyday workflow tools to deliver phishing, making detection harder for SOC teams.
Researchers propose BUD, per-block write-log digests enabling blockchain validators to serve historical membership and exclusion proofs far cheaper than state-wide tries.
The paper introduces Block Update Digests (BUD), which authenticate each block's write log with predecessor pointers, plus a SuperBUD and exponential hierarchy to turn long unchanged intervals into short proofs. Soundness against adversarial provers and up to f Byzantine validators is proven under archive, attestation, and committee evidence assumptions. Benchmarks show a 50x state-size increase raises the base-BUD path only 1.24x versus 3.1x for in-memory and 69.5x for disk-backed Merkle Patricia tries, with read payloads below 800 bytes and p99 warm verification at 146 microseconds.
Panel is an open-source research workspace where AI agents dynamically build their own panes, posted on Hacker News.
Panel, shared as a Show HN project on GitHub, is a research workspace in which the agent can construct its own panes rather than using a fixed interface. The post received 41 points and 8 comments on Hacker News. It targets agentic research workflows with a dynamically generated UI.
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.
PANORAMA grounds vision-language caption phrases in pixel-level masks via mask proposal selection, achieving state-of-the-art grounding on the new PanoCaps benchmark.
The paper introduces panoptic grounded captioning, requiring VLMs to describe foreground and background regions while grounding each phrase with pixel-level masks. Contributions include PanoCaps, a human-annotated benchmark built from panoptic segmentation datasets with entity-level image-text alignment, a phrase-mask matching protocol, and a generalized Panoptic Quality metric. PANORAMA conditions a pretrained segmenter on contextualized phrase representations to select masks, achieving the best overall grounding on PanoCaps; code, data, and models are released.
Malicious versions of the Rust crate arrayref executed a compile-time backdoor, with infrastructure overlapping recent DPRK supply chain attacks on Mastra and axios.
Wiz researchers report that malicious versions of the arrayref Rust crate ran a backdoor during compilation, compromising developer build pipelines. The campaign's command-and-control infrastructure significantly overlaps with infrastructure used in recent DPRK supply chain attacks, including campaigns against Mastra and axios. Developers using affected crate versions should audit their builds and review dependencies.
Hackers stole over €35 million from 500+ French notary offices in a four-year BEC campaign; ANSSI spent two years helping evict the attackers.
A business email compromise campaign breached more than 500 French notary offices — about 7% of all French notaries per the Conseil Supérieur du Notariat — over four years, stealing more than €35 million by phishing initial access and silently modifying wire transfer details. France's cybersecurity agency ANSSI worked for two years behind the scenes to help notaries remove the persistent attackers, who had deep access; officials also feared hackers could issue fake notarized acts such as marriage certificates or forged real estate deals. No forged documents have been found so far, but notaries have added two-factor authentication and in-person requirements for banking details, and banks added extra checks in 2024. The newsletter also notes other incidents, including a $320 million Bitcoin extraction from Blockstream's Liquid Network and a JetBrains Cadence breach via TeamCity servers.
Researcher proposes token efficiency (string length divided by BPE token count) as a better post-regex filter than entropy for secrets scanning, validated on CredData.
The post explores whether Byte-Pair Encoding tokenization can replace Shannon entropy as the primary filter for candidate secrets captured by regex in tools like Gitleaks. It defines 'token efficiency' as string length divided by token count under the cl100k_base tokenizer; secret-like strings such as GitHub tokens tokenize into many small tokens and score low, while natural text scores high. Evaluating labeled secrets from the CredData dataset shows a usable separation, with roughly 2.5 suggested as a minimum cutoff versus Gitleaks' 3.5 entropy threshold. The technique is positioned as a post-regex filtering step rather than a standalone detector.
Joint Tenable-SentinelOne analysis of 93 CVE-actor attribution pairs shows nation-state and criminal groups independently converge on the same edge infrastructure.
Tenable and SentinelOne jointly analyzed 93 CVE-actor attribution pairs covering exploitation of perimeter devices. The data shows state-sponsored and financially motivated actors independently target the same edge products from Ivanti, Fortinet, and Palo Alto Networks. The findings challenge the narrative that edge exploitation is exclusively a China-nexus nation-state problem, showing a broader shared attack surface.
A measurement study of 179 remote MCP servers finds heavy infrastructure concentration (HHI 0.736) and a security-observability tradeoff in platform OAuth.
The paper introduces a three-tier observability framework (catalog metadata, passive compliance signals, live vulnerability analysis) applied to a stratified sample of 179 remote Model Context Protocol (MCP) endpoints from two public registries. The Herfindahl-Hirschman Index over ASN distribution is 0.736, well above the 0.25 high-concentration threshold, and 95% of commercial PaaS-hosted servers enforce gateway-level OAuth 2.1 with PKCE. Authentication correlates strongly with hosting platform choice rather than operator configuration, creating a security-observability tradeoff that constrains automated scanning for tool-poisoning vectors without prior credentials.
Interpretability study traces how Qwen, Llama, and Gemma route query information and internal knowledge across layers when answering questions.
Researchers used layerwise interventions on hidden states to separate query-routing signals from target knowledge in language models, testing Qwen, Llama, and Gemma on country-continent questions with varied answer types. A pair-conditioned request direction strengthens before interventions alter downstream knowledge, opening a causal window while answer-supporting content is still forming. Trajectories differ by model: Gemma shows a partially overlapping mid-layer routing profile, while Llama has no sustained routing-effect window under the same gates.
MicroHasTEE expresses TrustZone secure and non-secure firmware as one typed Haskell program, catching peripheral-ownership inconsistencies at compile time.
MicroHasTEE is a multiparty Haskell framework that models both TrustZone firmware images as participants in a single typed program, using type-level capability ledgers to track peripheral acquisition, configuration, transfer, and finalization. MicroHs compiles the shared program twice to produce separate bare-metal Secure and Non-secure firmware images. The framework rejects inconsistent resource use, post-configuration attribution changes, wrong-domain callbacks, and calls to unregistered Secure services. A door-lock case study on an STM32U5 Nucleo board produced images of 232.7 KiB and 228.4 KiB of flash with roughly 220 KiB of SRAM per domain.
VDiff-Bench, a 1,756-question benchmark, shows multimodal LLMs struggle with fine-grained image-difference identification, scoring as low as 8.7% on low-level changes.
VDiff-Bench is a multiple-choice benchmark of 1,756 four-way questions over image pairs covering 10 change categories including position, motion, color, texture, OCR/text and illumination, with curated hard negatives. Evaluation of 11 state-of-the-art open- and closed-source MLLMs shows fine-grained visual comparison remains brittle: 7-8B-scale open-source models score 52.5-70.6% on semantic changes but only 8.7-33.3% on low-level changes like noise and texture. Notably, Grok 4.3 shows a sharp performance drop on noise and texture differences, falling behind large open-source models like Kimi K2.5 and K3.
Researchers release NEST-Phish, a synthetic enterprise phishing email dataset with matched legitimate and phishing emails and cue annotations for detection research.
Academic researchers introduce NEST-Phish, a publicly released synthetic enterprise phishing email dataset built around a fictitious organization named Nebulon. It contains matched synthetic legitimate and phishing emails across a broad set of workplace communication themes, each with interpretable phishing-cue annotations. Human-subject categorizations and supervised classifier evaluations indicate the dataset supports meaningful variation in phishing judgments and provides learnable signal for detection models. It is intended to support work on phishing detection, human susceptibility, explainability, and benchmark development.
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.
Rapid7 analyzes how fraud marketplaces are fragmenting into specialized shops after larger marketplaces were dismantled, aided by new MITRE F3 framework
Rapid7 reports a shift from large known fraud marketplaces to a fragmented environment of smaller specialized storefronts such as Xleet, Blackpass, Infodig, and Styx, operating across dark web channels, Telegram, and P2P options. These Fraud-as-a-Service shops sell stolen accounts, PII, synthetic identity generation, infrastructure, and money laundering support, supporting schemes like business email compromise. MITRE's Fraud Fighting Framework (F3), introduced in early 2026, aims to help security teams prioritize monitoring of fraud TTPs, particularly account takeover techniques. Fraud damages are anticipated to approach hundreds of billions of USD.
A researcher disclosed nine vulnerabilities in ATM encryption and authentication software, highlighting weaknesses across the software supply chain.
WIRED reports that a security researcher found nine vulnerabilities in software used for ATM encryption and authentication. The disclosure matters beyond cash machines because the affected components illustrate broader weaknesses in the software supply chain. The article does not report active exploitation of the flaws.
An arXiv paper claims a method that breaks the 1.58-bit barrier for ternary large language models.
The arXiv preprint 2609.16338, titled 'Breaking the 1.58-bit Barrier for Ternary LLMs,' presents research on ternary-weight large language models, which use roughly 1.58 bits per weight. The source text contained only the title and Hacker News engagement data (56 points, no comments), so further technical details are not available.
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.
Introduces VEX-Bench, 75 expert-labeled real-world cases testing whether LLM agents can assess supply chain vulnerability exploitability; frontier models reach about 80% F1.
VEX-Bench is the first benchmark evaluating LLM agents on assessing whether upstream dependency vulnerabilities are exploitable in downstream projects, with 75 real-world expert-labeled cases across Python, Java, and Go mined from GitHub. Nine models across three agent harnesses were evaluated; GPT-5.5 and Claude Opus 4.6 reach approximately 80% F1 on binary vulnerability-status classification, but only GPT-5.5 surpasses 70% macro-F1 on fine-grained justification classification. The gap highlights the difficulty of moving beyond binary exploitability calls to explaining exploitability reasons, unlike prior benchmarks targeting zero-day settings.