Search: “release-cycle”

Malicious LiteLLM Releases Tied to Trivy Hack May Have Exposed 2,100+ Organizations

Malicious LiteLLM 1.82.7/1.82.8 PyPI releases tied to the Trivy TeamPCP campaign harvested cloud, SSH, and database credentials, potentially exposing 2,500+ organizations.

CloudSEK reported that two malicious LiteLLM releases on PyPI (versions 1.82.7 and 1.82.8, live about 40 minutes on March 24) harvested cloud keys, SSH keys, Kubernetes tokens, and database passwords, with captured loot files mapping potential exposure to more than 2,500 organizations including NVIDIA, Cisco, Deloitte, Volkswagen, FedEx, Siemens, and X Corp. The campaign is part of TeamPCP (tracked by Google as UNC6780), linked to the Aqua Security Trivy scanner compromise tracked as CVE-2026-33634 and added to CISA's Known Exploited Vulnerabilities catalog on March 26. The payload used a litellm_init.pth file executed at Python interpreter startup and exfiltrated secrets to models.litellm[.]cloud; the FBI's FLASH-20260702-01 advisory urged rotation of CI/CD, publishing, and cloud credentials.

The Hacker News·Aug 12, 08:05 UTC · Aug 12, 2026Data breach in the wildCVE-2026-33634

Package Manager Trends

Sixteen-week roundup finds package managers converging on release-age cooldowns, install-script blocking, malware scans, and recurring path-traversal and credential-leak fixes.

The author aggregates supply-chain security trends from sixteen weeks of This Week in Package Management, built from about 80 RSS feeds. Release-age cooldown gates shipped in Deno 2.8, Bundler, npm, Yarn, mise, Hex, Mamba, and Cargo, with Dependabot making a three-day cooldown default in August. npm 12 and Bun 1.4 now block lifecycle install scripts by default, and Composer 2.10 and uv added install/publish-time malware checks, while npm's registry began scanning at publish time. Path traversal on archive extraction was fixed in 14 of 16 weeks across tools including uv, pnpm, Docker, and Composer, and credential-misdirection bugs affected Cargo, ORAS, Composer, and Renovate.

Lobsters · security·Sep 10, 12:30 UTC · 5d agoResearch1

Chrome is now shipping updates every 2 weeks as AI changes the security landscape

Google switches Chrome to two-week release cycles starting with Chrome 153, shrinking the N-day patch gap as AI-driven threats accelerate.

Chrome officially moved from a four-week to a two-week release schedule with Tuesday's launch of Chrome 153 on desktop, iOS, and Android. Google ties the change to its evolving security strategy, saying faster releases shrink the N-day patch gap as AI tools and community bug reports increase patch volume. Mozilla, Microsoft, and Brave have already adopted the two-week cadence, and Google is also racing AI-native browsers like Brave, Dia, Opera Neon, and Perplexity's Comet while experimenting with AI features in Chrome.

TechCrunch · Security·Sep 8, 15:04 UTC · 7d agoIndustry

Chainloop: Open-source evidence store and policy engine for the software supply chain

Help Net Security·Aug 10, 00:00 UTC · Aug 10, 2026Policy & legal

The Closed Loop Remediation Playbook with Wiz

Wiz announces Workflows general availability and public preview of Remediation and Response for closed-loop, automated cloud remediation.

Wiz published a remediation playbook promoting a closed-loop approach to cloud security remediation. The company announced that Wiz Workflows is now generally available, while Remediation and Response capabilities are in public preview. The announcement is a vendor product release aimed at enabling self-healing cloud environments rather than reporting a new threat or vulnerability.

Wiz Blog·Aug 17, 12:23 UTC · Aug 17, 2026Tools

Connecting the Dots: Securing the Overlooked Corners of the Software Development Lifecycle (SDLC) Supply Chain

Unit 42 warns attackers increasingly target CI/CD pipelines and developer tools rather than application code, urging full SDLC supply chain visibility.

Palo Alto Networks Unit 42 research argues attackers are shifting focus from application code to overlooked corners of the software development lifecycle supply chain, including CI/CD pipelines and developer tooling. The write-up calls for total SDLC visibility and strict security controls to defend these developer-facing attack surfaces.

Palo Alto Unit 42·Aug 21, 23:00 UTC · 25d agoResearch in the wild

Ubuntu 24.04.5 LTS release patches security bugs across ten flavors

Canonical ships Ubuntu 24.04.5 LTS point release bundling security fixes into fresh install media for desktop, server and nine other flavors.

Canonical released Ubuntu 24.04.5 LTS, a point release for the Noble Numbat series that folds accumulated security corrections and high-severity bug fixes into new installation media. Nine flavors including Kubuntu, Xubuntu, Ubuntu MATE, Ubuntu Studio and Edubuntu also moved to 24.04.5. Existing 22.04 LTS users receive the fixes through the automatic upgrade path at no cost. The release notes name no CVEs or bug IDs, and support timelines still count from the original 24.04 launch date (five years for Desktop/Server/Cloud/Core, three for flavors, extendable with Expanded Security Maintenance).

Help Net Security·updated Sep 11, 12:03 UTC · 5d ago·first Sep 11, 04:07 UTC · 5d agoAdvisory 13 sources

There Is No Patch Tuesday on the Blockchain: Why Solidity Developers Need Fusion-Grade AppSec Before They Deploy

Checkmarx argues Solidity developers need pre-deployment AppSec because disclosure-to-weaponization time has collapsed from 840 days to 1.6 days.

A Checkmarx write-up contrasts traditional software patch cycles with blockchain development, where there is no Patch Tuesday and fixes require on-chain upgrades. It cites stats that median disclosure-to-weaponization time collapsed from 840 days to 1.6 days and that 80% of exploitations now occur on or before disclosure day. The piece advocates fusion-grade application security for Solidity teams before contracts deploy.

Checkmarx·Aug 17, 15:04 UTC · 29d agoResearch1

The History Is the Detector: Executing CVE Patch History, End-to-End

BUGSTONE-E2E converts CVE patch history into executable LLM-guided detection rules, yielding 1,033 rules and 644 runtime-verified findings across 14 programs.

The BUGSTONE-E2E framework mines reusable detection rules from verified fixing commits, organized by CWE and language, and applies them through a funnel pipeline that escalates from Tree-sitter anchors and lightweight heuristics to LLM-based agent inspection, runtime verification, and scope-checked patch generation. Built from 19,325 high-severity CVEs published 2022-2026, it produced 1,033 detection rules spanning 56 CWE families, packaged into 172 skills. Applied across 14 programs, it generated runtime evidence for 644 findings, demonstrating that vulnerability history can drive reproducible detection and repair.

arXiv cs.CR·Sep 4, 16:40 UTC · 11d agoResearch

What It Took to Reach 1 Billion Build Manifests

Chainguard doubled container build manifests to over 1 billion in six months, powered by Factory 2.0's agentic self-correcting rebuild system.

Chainguard reports growing from 500 million to over 1 billion container build manifests in six months, across more than 3,000 unique images and 675,000 image versions. Its Factory 2.0 system, built on the purpose-built Chainguard OS, uses an agentic reconciliation engine called DriftlessAF to decide when to rebuild across thousands of interdependent projects without human intervention. All artifacts ship with SLSA Level 3 provenance, Sigstore signatures, and full SBOMs.

The Hacker News·Sep 8, 11:49 UTC · 8d agoIndustry

New Report: AI threats are here. Why Q2 2026 signals the end of traditional patch cycles

Rapid7 Labs' Q2 2026 threat report finds vulnerability disclosures surging while AI-assisted attackers compress the time from disclosure to exploitation.

Rapid7 Labs' Quarterly Threat Landscape Report for Q2 2026 reports continued growth in vulnerability disclosures alongside attacker use of automation and AI-assisted tooling. The report argues the window between disclosure and exploitation is shrinking, eroding the value of traditional patch cycles. It recommends prioritizing exposures attackers can actually reach rather than attempting to patch everything.

Rapid7 Blog·Aug 18, 12:49 UTC · 28d agoResearch

macOS 27.0 RC (26A428)

Apple seeded the macOS 27.0 Release Candidate (build 26A428) to developers ahead of the final public release.

Apple published a Release Candidate build of macOS 27.0, labeled 26A428, on its developer release page. The notice only provides download and release-notes links and includes no security content, CVEs, or threat information. RC builds typically precede the general availability of the final operating system version.

Apple software releases·updated Sep 14, 17:00 UTC · 1d ago·first Sep 9, 17:00 UTC · 6d agoAdvisory 2 sources

How Developers Prevent Production Risk at the Source

Wiz argues shift-left code-stage security cuts remediation costs and ships Wiz Code, WizOS, and WizCLI to catch flaws pre-deployment.

A Wiz blog post argues that fixing vulnerabilities at the code stage costs a fraction of production remediation, citing the node:20-slim base image which ships 14 known CVEs, three of them critical. It claims AI agents can now analyze open-source commits and generate working exploits in minutes, shrinking the window between a public fix and automated attacks. The post promotes WizOS near-zero-CVE base images, WizCLI and MCP-server checks inside developer and AI agent sessions, and Wiz Code scanning across pull requests, repositories, and pipelines covering SAST, SCA, secrets, and IaC misconfigurations.

Wiz Blog·Sep 3, 00:31 UTC · 13d agoIndustry1

Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction

ICF-DLM, the first language-model-based inertial confinement fusion predictor, cuts peak-timing error from 11.6 to 9.2 steps versus LLaMA-3-8B.

Each National Ignition Facility shot costs roughly one million dollars, motivating accurate AI surrogates for predicting 512-step neutron-rate waveforms from laser pulses and target parameters. ICF-DLM combines physics-typed decomposition into yield, peak timing, and local waveform; bidirectional denoising that defers commitment to peak location; and a physics-driven PPO reward. On ICFBench (50,000 simulations plus 232 experimental shots) it outperforms a matched autoregressive LLaMA-3-8B, classical sequence models, and LLM-based time-series predictors.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:00 UTC · 8d agoAI research

When the World Lies: Backdoor Attacks on Latent World Models for Downstream Control

A poisoned world-model checkpoint hijacks downstream controllers without an explicit trigger rule, passing clean-data evaluation while steering 100% of triggered actions.

Researchers show that a released pretrained world-model checkpoint acts as a supply-chain backdoor for downstream control. The poisoned model routes trigger-bearing observations into a chosen latent region and reshapes dynamics so the victim's own Dreamer-style actor training or MPC/CEM planning re-discovers attacker-targeted actions. The attack hijacks 100% of triggered steps in the strongest settings while retaining roughly 75% clean-task success and passing standard clean-data diagnostics. Moderate clean fine-tuning fails to remove the backdoor without substantially degrading clean control.

arXiv cs.CR·Sep 14, 16:01 UTC · 1d agoAI safety & security

Closing the Blind Spot: Securing Personal Repositories in the Software Supply Chain

Wiz highlights personal developer repositories as a supply chain blind spot leaking corporate secrets, offering correlation-based risk validation and remediation.

Wiz argues that developers' personal code repositories are a blind spot in software supply chain security where corporate secrets quietly escape. The company describes an approach that correlates personal repositories to specific developers, validates the actual risk, and drives remediation. No specific incident or vulnerability is disclosed in the announcement.

Wiz Blog·Aug 13, 13:48 UTC · Aug 13, 2026Tools2

Model-Aware Schedules Improve Generation via Fiberwise Optimal Transport

Researchers introduce model-aware diffusion schedules via fiberwise optimal transport, cutting flow-matching FID on CIFAR-10 by 38.6% at 16 function evaluations.

The paper proposes constructing diffusion and flow-matching sampling schedules from a fiberwise prediction risk defined via optimal transport, combined with coefficient-path kinetic action, yielding a closed-form time allocation. Across DDPM and flow-matching experiments spanning targets, datasets, and architectures, the schedules beat model-agnostic baselines, including a 38.6% relative FID reduction for flow matching on CIFAR-10 at 16 function evaluations. Normalized fiberwise-risk profiles from independently trained models align closely, suggesting empirical universality, and a frozen analytic allocation template retains most of the gains.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:30 UTC · 5d agoAI research

Show HN: Sunk Cost – How long until a local LLM rig pays for itself?

Show HN tool 'Sunk Cost' calculates when a local LLM rig breaks even versus falling API prices, factoring electricity cost and inference speed.

A Hacker News Show HN project called Sunk Cost models the payback period of buying local LLM hardware instead of paying API prices. Users can adjust assumptions like electricity cost ($/kWh) and API speed (tokens/second), and the model assumes API prices keep falling. Where local speed is unmeasured, it is estimated from memory bandwidth divided by bytes read per token, and labelled as an estimate.

Hacker News · security·Sep 15, 01:37 UTC · 1d agoAI tools & infraHN 30↑ · 31 comments

One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation

A review paper frames on-policy self-distillation collapse as governed by three levers: token weighting, privileged information, and guidance decay.

The paper critically reviews On-Policy Self-Distillation (OPSD), where a language model trains on its own generations scored token-by-token by a teacher conditioned on privileged information such as reference solutions or environment feedback. It identifies collapse, the progressive narrowing of producible reasoning paths, as the dominant failure mode and analyzes it through three levers: signal weighting, the nature of privileged information, and teacher dynamics. The review is restricted to mathematical reasoning, reports no new experiments, and offers a shared vocabulary separating settled findings from disputed ones.

Hugging Face daily papers·Aug 25, 20:00 UTC · 21d agoAI research

Decomposition Buys Integrity, Not Yield

Study of 600 production deep-research traces finds agent-tree decomposition loses findings at rate N^(1-δ); flat architectures maximize yield.

The paper models multi-agent decomposition as a tree where an agent holding b items retains each with probability r(b); with r(b)=1/b every tree delivers exactly one finding regardless of shape. Analysis of 600 production deep-research traces estimates delta=0.34 retention decay, and 1,012 annotated traces show one brief in sixteen goes off-target per tier, giving an alignment penalty of 0.536. Depth still cuts root context exposure from N to N^(1/k) and is cheaper at scale, with a hazard model over 743,819 production tool calls showing delegation is an opening move rather than a response to filling context.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:04 UTC · 19h agoAI research

Release Notes: Faster TI Investigations, Fresh Threat Research, and 650+ Threat Coverage Updates

ANY.RUN August release adds TI Lookup connections view, 81 behavior signatures, 16 YARA rules, 559 Suricata rules, and three new threat intelligence reports.

ANY.RUN released August product updates expanding its Threat Intelligence Lookup with a Connections block for pivoting between related observables (domains, IPs, URLs), JSON export for retrohunting and SIEM/NDR integration, and hidden whitelisted data by default. Detection coverage grew with 81 new behavior signatures, 16 YARA rules, and 559 Suricata rules covering malware execution, phishing, and C2 traffic. Three new Threat Intelligence Reports cover a US-focused RMM phishing campaign across 46 countries, the Mirage2FA phishing-as-a-service targeting Microsoft 365 (1,249 sandbox sessions, 9,332 potential compromise events), and a threat brief on OVERLORD RAT, CRPX0, and TRIBACK loader.

ANY.RUN·Sep 3, 09:11 UTC · 13d agoTools1

State of Open Models: Summer 2026 Observations

Hugging Face publishes observations on the state of the open-weights model ecosystem as of summer 2026.

A Hugging Face blog post titled 'State of Open Models: Summer 2026 Observations' surveys developments across the open-weights model ecosystem. No article text was available, so specific model releases, benchmarks, and findings are not detailed here.

Hugging Face Blog·Aug 14, 00:00 UTC · Aug 14, 2026AI industry

VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities

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.

arXiv cs.CR·Sep 7, 22:52 UTC · 8d agoResearch1

Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems

16-day multi-agent stress test finds no world fully resilient to prompt injection, misinformation, or memory exposure; adversarial content acted on 46 hours later.

Emergence World is a continuously running multi-agent environment for adversarial stress testing of long-horizon autonomous systems. Eight parallel 10-agent worlds (seven homogeneous frontier-model worlds plus one mixed-model world) ran for 16 days, generating over 850,000 LLM calls and nearly 50 billion tokens. Three controlled stress events—indirect prompt injection, misinformation, and exposure of private agent memories—were delivered through ordinary interaction surfaces; no world achieved full resilience. Detection did not ensure containment: agents recognized threats yet wrote adversarial content into persistent memory and acted on it up to 46 hours later, suggesting model-level alignment is not compositional.

Hugging Face daily papers·Sep 14, 20:00 UTC · 1d agoAI safety & security

Signed Rescue Routing: Harm-Aware Cascades for Efficient LLM Inference

Signed Rescue Routing improves LLM cascade efficiency by predicting when a larger model actually corrects a smaller one rather than uncertainty.

Signed Rescue Routing (SRR) is a budgeted cascade method that separately predicts rescues and regressions when escalating from a small to a large model, ranking requests by their signed difference. The authors prove this signed conditional gain is Bayes-optimal under a fixed escalation budget and add only a lightweight two-head router needing small-model output statistics at deployment. Evaluation with Qwen3-4B and Qwen3-8B on MMLU, HellaSwag, and ARC-Challenge shows better accuracy-compute tradeoffs than entropy routing and learned error predictors.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:31 UTC · 8d agoAI research

Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.

Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:31 UTC · 6d agoAI research

Version Control DFIR: a Cheatsheet to GitHub, GitLab, Bitbucket, and Azure DevOps

Wiz published a DFIR cheatsheet covering log visibility, incident readiness, and threat hunting across GitHub, GitLab, Bitbucket, and Azure DevOps.

Wiz researchers released a practitioner's guide to version control system forensics, incident response, and threat hunting. The cheatsheet maps log sources, audit capabilities, and hunting workflows across GitHub, GitLab, Bitbucket, and Azure DevOps. It aims to improve incident readiness for source code and CI/CD compromise scenarios.

Wiz Blog·Aug 27, 12:00 UTC · 20d agoResearch1

Agnes-AI/Agnes-3.0-Flash — new model trending #30 on Hugging Face

Agnes AI releases open-weight Agnes-3.0-Flash Preview, a 33B multimodal model with 262k-token context under Apache 2.0.

Agnes AI released Agnes-3.0-Flash Preview, an open-weights multimodal checkpoint with 33B parameters and a 262,144-token context window under Apache 2.0. The model supports text, image, and video understanding, tool calling, and adjustable reasoning effort. The repo clarifies this preview checkpoint is distinct from the production/API Agnes 3.0 Flash model, which uses a different configuration with a 1M-token context window. Reported reference results include IFBench 74.20 and SciCode 38.08 against peers such as Qwen3.6-35B-A3B, Kimi K2.5, and MiniMax M3.

Hugging Face trending models·Sep 11, 15:50 UTC · 4d agoModel release

LiteLLM Supply-Chain Attack - Technology, Banking and Healthcare the Most Affected

TeamPCP planted the SANDCLOCK credential stealer in LiteLLM PyPI releases, exposing credentials across 2,038 repositories at 898 organizations including Microsoft and NVIDIA.

Threat actor TeamPCP compromised LiteLLM maintainer credentials and published malicious versions 1.82.7 and 1.82.8 to PyPI around March 2026, creating an exposure window of several months. The SANDCLOCK credential stealer exposed full credential sets across 898 GitHub owners and 2,038 repositories, including Microsoft, Azure, IBM, NVIDIA, PayPal, Deloitte, Bosch, and S&P Global. Stolen material includes GitHub CI/CD identities, AWS/GCP/Firebase credentials, SSH keys, Kubernetes secrets, and OpenAI and Anthropic API keys; Resecurity acquired a 150GB archive with 2,146 credential records. Technology, banking/finance, and healthcare organizations are the most affected sectors, and victims must rotate all exposed credentials.

Security Affairs·Aug 17, 17:09 UTC · 29d agoMalware in the wild

RESCUE-BENCH: Towards Relation-Aware Multi-Party Emotional Support Conversation Systems

Researchers introduce RESCUE-Bench, a video benchmark of 191 couple and family conversations evaluating LLMs on relation-aware multi-party emotional support.

RESCUE-Bench is built from real couple and family interview conversations, containing 191 samples, 7,079 annotated turns, and 1,064.8 minutes of video. It defines six tasks measuring two capabilities: Relational Understanding and Relation-Sensitive Support. Experiments with ten LLMs show models handle local emotional cues but struggle with relation pattern prediction, viewpoint prediction, and support strategy prediction.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research