Search: “artifact-revision”

Re: Retrospective by 'gpg.fail' authors

oss-security thread on the gpg.fail retrospective discusses legacy code removal and flaws surfaced by frontier AI models in open-source projects.

In a reply to Soatok Dreamseeker's gpg.fail retrospective thread on the oss-security mailing list, cryptographer Peter Gutmann argues it is often unclear which legacy code is obsolete or can be safely removed. He recounts a discussion with another well-known open-source project developer estimating, unscientifically, that around half of issues in such projects were found by frontier AI models. The exchange reflects on lessons from documenting weaknesses in GnuPG/OpenPGP implementations.

oss-security·03:19 UTC · 1h agoResearch 9 sources

Artifactory Under Attack: In-the-Wild Exploitation of CVE-2026-42016, CVE-2026-42018 & CVE-2026-82329

Wiz Research confirms in-the-wild exploitation of three JFrog Artifactory vulnerabilities, chained to gain administrative control, deploy Groovy plugins, and install Rust backdoors.

Wiz Research identified active exploitation of CVE-2026-42016, CVE-2026-42018, and CVE-2026-82329 in JFrog Artifactory between August 15 and September 8, 2026. Attackers chain the anonymous-token exposure (CVE-2026-42018) with the token scope-validation flaw (CVE-2026-42016) to obtain admin-scoped tokens, while CVE-2026-82329 allows unauthenticated administrative access in default configurations. Observed post-exploitation includes persistent administrator accounts created in under five minutes, malicious Groovy plugin deployment, ad-hoc command execution, Rust-based C2 backdoors dropped to writable paths, and webshell uploads. Wiz measured that 59-62% of organizations running Artifactory remained vulnerable to the chained CVEs weeks after disclosure, and the vulnerabilities were already included in CISA KEV.

Wiz Blog·updated Sep 14, 09:27 UTC · 2d ago·first Sep 10, 19:04 UTC · 6d agoExploit / PoC in the wild 7 sourcesCVE-2026-42016CVE-2026-42018CVE-2026-823292· 1 read

Attackers Pounce on Critical Artifactory Bug Following Disclosure

Attackers began exploiting CVE-2026-82329, a critical authentication bypass in JFrog Artifactory, shortly after disclosure, gaining admin-level access on affected systems.

CVE-2026-82329 is a critical authentication bypass in JFrog's Artifactory repository manager that enables attackers to gain admin-level access on affected systems. Exploitation started soon after the flaw's disclosure. Artifactory is widely used to manage software artifacts, making compromised instances a software supply chain risk.

Dark Reading·Sep 1, 21:05 UTC · 15d agoExploit / PoC in the wild 2 sourcesCVE-2026-82329

What Else Needs Fixing? Exploring Cost-Effective Test-Time Compute for Revision Propagation in Artifacts Generated Through Conversation

A new benchmark shows LLMs reach 68.3-93% accuracy propagating local revisions across conversationally generated artifacts, with parallel-sample selection most cost-effective.

The paper introduces a benchmark for revision propagation: when users request a local change, LLMs must identify dependencies and update all affected parts of an artifact generated through conversation, where context lives in the chat history. Nine revision methods, including sequential reflection and parallel sampling variants, were evaluated on gpt-oss-20b/120b, gpt-5.4-mini, and qwen3.5-9b/27b/122b. Baselines scored 68.3-93% accuracy, and selecting among three parallel samples via LLM-based or medoid selection improved accuracy by 2.2-9.7% as the most cost-effective test-time compute strategy. Code and dataset are released.

Hugging Face daily papers·Sep 2, 20:00 UTC · 14d agoAI research

Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability

A controlled study finds agent memory portability varies sharply: fixed-schema knowledge graphs survive model swaps while compressed notes degrade.

The study compares preserving an agent's history as raw long context, RAG chunks, compressed natural-language notes, or fixed-schema knowledge graphs across model upgrades, using 48 synthetic histories and two open-weight sub-10B-parameter models. Fixed-schema KG accuracy changed by only +0.0004 ± 0.0020 after a writer swap, while compressed NOTES shifted asymmetrically by +9.91 or -13.28 percentage points depending on migration direction. Mixed 50/50 embedding migrations captured only 4.96 of an 11.90-point RAG re-embedding gain; 80% of the NOTES deficit came from information lost at construction, and 81% of the RAG deficit from retrieval failures. Store-only repair of NOTES failed to reach 90% recovery in all 48 cases, while retaining raw histories enabled recovery in 34 of 48 for one direction.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:44 UTC · 12d agoAI research1

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 · 12d agoResearch

Three in four AI-generated vulnerability patches leave something broken

Help Net Security·Aug 6, 00:00 UTC · Aug 6, 2026VulnerabilityCVE-2026-8512

When Models Edit Too Much: On the Fidelity of Minimal Code Edits

A 400-task BigCodeBench evaluation shows frontier LLMs widely over-edit code; a preservation instruction cuts excess edits and raises Pass@1 by 2.3 points.

Researchers built an evaluation framework from 400 BigCodeBench problems with injected AST-level corruptions, each with a known minimal patch, to measure over-editing in LLM code repair. Even strong models like GPT-5.5 produce unnecessarily large edits despite high Pass@1. Adding a preservation instruction reduced average excess Levenshtein distance from 0.195 to 0.131, cut added cognitive complexity by 26.6%, and raised Pass@1 by 2.3 points. Reinforcement learning post-training gave the best out-of-domain edit-fidelity trade-off, while supervised fine-tuning overfit to seen corruption patterns.

Hugging Face daily papers·Sep 2, 20:00 UTC · 14d agoAI research1

Homebrew 7.0.0 Adds Built-In Vulnerability Scanner and Stronger Package Sandboxing

Homebrew 7.0.0 ships a native brew vulns scanner, OSV-backed advisory database, stronger sandboxing, and fixes a sandbox-escape issue.

Homebrew 7.0.0 introduces a built-in 'brew vulns' command that scans installed formulae, dependencies, and Brewfiles against a new Homebrew-specific advisory database and OSV.dev data, distinguishing backported fixes from outdated versions. The release hardens sandboxing by blocking home-directory reads during builds, disabling network access at install time, and replacing Bubblewrap with Landlock on Linux. It also fixes moderate-severity GHSA-5263-whxq-77hp, where a malicious cask could execute code outside the macOS sandbox via LaunchServices, and drops macOS Catalina while moving Intel Macs to Tier 3.

Cyber Security News·Sep 15, 13:55 UTC · 1d agoTools1

Revoked but Still Authoritative: An Empirical Study of Revocation Enforcement in Agent-Memory Systems

An empirical study finds no major agent-memory system enforces fact revocation at retrieval, causing agents to act on superseded, unsafe information.

Researchers tested five agent-memory systems across nine policy scenarios, nine models, and six defense conditions, tracking whether revoked facts are returned and acted upon. No system enforces revocation by default: revoked records are returned whenever the revocation label is visible to the retrieval layer, outrank their replacements, and lead agents to unsafe actions. The authors propose a backend-agnostic guard that sits between the agent and any memory store and withholds revoked or conflicting records at retrieval time.

arXiv cs.CR·Sep 8, 05:03 UTC · 9d agoAI safety & security

Senior engineers are spending their week cleaning up AI-generated code

New Relic study finds AI-generated code doubles critical runtime issues, with senior engineers losing a third of their week to fixes.

A New Relic survey of U.S. technology leaders reports AI now writes the majority of shipped code, with senior SRE and DevOps engineers spending up to a third of their week triaging and refactoring it. A large majority of organizations had at least one AI-related production failure in the past six months, and roughly three in ten saw newly introduced security vulnerabilities. AI-generated code showed nearly twice as many critical runtime issues as peer-reviewed human-authored code, with gaps concentrated in edge cases, concurrency, deprecated APIs, and complex state changes. Most teams now prompt AI tools to embed logs and traces directly into generated code.

Help Net Security·Aug 24, 04:22 UTC · 24d agoAI industry1

1Password's AI patching benchmark is misleading

Trail of Bits reanalysis says 1Password's 26% AI clean-fix rate is misleading; 86% of eligible patches blocked exploits.

Trail of Bits critiques 1Password's FLAWED AI patching benchmark, arguing its 26% clean-fix headline mixes trials where agents were instructed to apply wrong fixes (22% of data) with trials that prohibited compiling or testing (36%). Restricting to reasonable conditions, 2,634 of 3,067 patches (86%) blocked the supplied exploit. Trail of Bits also reports 12.5% of 2,265 developer first fixes failed in its own 2024-2026 assessments, and released post-patch-validation and review-walkthrough agent skills.

Lobsters · security·Sep 15, 20:03 UTC · 1d agoResearch1

Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication

UID-preserving multimodal framework plus GAVEL LLM judge improves clinical timeline reconstruction, boosting event recovery 43% over prior matching.

The paper introduces a UID-preserving framework linking each narrative clinical event to its source span through text-only estimation, structured-evidence retrieval, timestamped source-row grounding, and joint revision. GAVEL, an LLM judge, compares UID-aligned timelines against narrative and structured records. Across six open-weight models and 40 mixed-critical-care summaries, GLM 5.2 multimodal revision improved temporal agreement without reducing event recovery and performed competitively with clinician annotations, while DeepSeek V3.2 did not benefit from multimodality. The pipeline achieves 43% increased event recovery with occurrence-level provenance.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:01 UTC · 5d agoAI research1

ReCite: Agentic Reasoning for Faithful Citation

ReCite is an agentic citation framework using claim-level reasoning and verification, outperforming large generative models in strict citation accuracy.

ReCite is a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification for citation recommendation. Trained on synthesized reasoning trajectories, the agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments show the lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy, addressing misattribution where cited papers are real but logically unsupportive.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:59 UTC · 8d agoAI research

An Evidence Model for Agentic Processes: Evidence Claims, Trust Assumptions, and Policy Assessment

Researchers propose an evidence claim model defining which trust and audit claims agentic AI systems can support, mapping claims to mechanisms, assumptions, and threats.

The paper proposes an evidence claim model for agentic AI processes that exchange messages, invoke tools, request approvals, and modify shared artifacts. It distinguishes claim types such as artifact integrity, provenance, approval evidence, and policy assessment, mapping each to mechanisms, assumptions, limitations, and threats. It stresses that hashes, signatures, and external anchors do not establish semantic truth, authorization, or capture completeness. The contribution is conceptual, offering vocabulary for what an agentic black box can and cannot evidence and which controls must surround it.

arXiv cs.CR·Sep 8, 09:25 UTC · 8d agoAI safety & security

ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation

ActReview post-trains Qwen3-8B-Base on OpenReview rebuttals to generate actionable peer-review feedback with grounded revision suggestions, benchmarked on 1,000 curated instances.

The paper defines Actionable Peer-review Generation as diagnostic claim generation plus revision suggestion generation and introduces ActReview, a rebuttal-guided post-training framework. From OpenReview review-rebuttal threads the authors build ActReview-40K, aligning reviewer weaknesses with author responses grounded in localized paper evidence, and post-train Qwen3-8B-Base with multi-task SFT followed by GRPO using weakness-specific rubric rewards. They also release ActReview-Bench, a human-curated 1,000-instance benchmark, on which ActReview outperforms prior specialized review-generation models on actionability and grounding while remaining competitive with strong prompt-based LLMs. Human evaluation confirms improved revision usefulness but identifies a remaining gap in technical accuracy.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research

Re: Vulnerabilities fixed in libxml2-2.15.4

libxml2 2.15.4 patches two flaws including a heap buffer overflow in xmlDictAddQString tracked as CVE-2026-86137 and CVE-2026-86138.

libxml2 releases before 2.15.4 are affected by an integer overflow in xmlDictAddQString in dict.c that leads to a heap-based buffer overflow, tracked as CVE-2026-86137 and CVE-2026-86138. The oss-security post from Debian's Salvatore Bonaccorso flags the fixed release for downstream tracking. No exploitation is mentioned in the disclosure.

oss-security·Sep 5, 12:16 UTC · 11d agoVulnerabilityCVE-2026-86137CVE-2026-86138

Forgetting Without Restarting: Execution-State Unlearning for Stateful LLM Agents

Researchers propose provenance-guided selective replay letting LLM agents forget revoked information without restarts, matching full reset behavior.

The paper formalizes execution-state unlearning for stateful LLM agents, requiring that agents behave as if a revoked memory record was never observed across transcripts, compressed memory, tool plans, and KV caches. It proves exact unlearning requires at least T-τ+1 recomputed transitions and that Provenance-Guided Selective Replay attains this bound via a provenance graph, KV cache cropping, and sanitized replay. In audits across three agent suites, nine baselines, and three model families, memory deletion left leakage unchanged, instruction-based forgetting collapsed under elicitation (Leak@probes = 1.00), and selective replay matched full resets at up to 9x fewer recomputed tokens.

arXiv cs.CR·Sep 4, 08:31 UTC · 12d agoAI safety & security1

When LLM Decompilers Recompile More and Preserve Less

Researchers show LLM decompiler outputs can recompile yet diverge behaviorally, proposing the Decompile-Diverge fuzzing oracle to catch hidden changes.

The paper demonstrates that LLM-based decompilers can produce code that recompiles and passes all shipped tests yet diverges on other legitimate inputs—4.9% overall and up to 13% for one system—and can make disclosed vulnerabilities vanish without a visible crash. Across 300 real GitHub functions and 287 CVE-grounded functions, a refinement LLM lifted Ghidra's build rate from 75% to 90% while Matched rate fell from 74% to 62%, with up to one tenth of vulnerabilities showing Crash Absence. Decompile-Diverge detects these gaps by synthesizing drivers, growing fuzzing corpora from the reference, and rerunning decompiled code on identical inputs.

arXiv cs.CR·Sep 4, 17:16 UTC · 12d agoResearch

The Vulnerability Gap: Why Discovery Is Outrunning Repair

Dark Reading argues AI-accelerated vulnerability discovery and tightening regulation are widening the gap between flaw discovery and repair capacity.

The article argues that AI tooling is increasing the pace at which vulnerabilities are discovered while remediation capacity has not kept up, creating a growing backlog. It frames this widening 'vulnerability gap', combined with a tightening regulatory environment, as an all-hands-on-deck moment for security teams. The piece is analysis and opinion rather than disclosure of a specific flaw.

Dark Reading·Aug 24, 14:00 UTC · 23d agoIndustry

Adversarial Testing of Automated Program Repair Agents for Security Vulnerabilities

SWEADV benchmark shows adversarial issue descriptions make LLM program-repair agents write insecure fixes in 51.7% of cases, evading most detection tools.

Researchers built SWEADV, a benchmark of 750 adversarial issue descriptions derived from 150 SWE-bench Verified repair tasks, covering command execution, deserialization, path traversal, denial of service, and weak hashing attack types. Tested on mini_swe agents backed by GPT-5-Mini, MiniMax-M2.5, and DeepSeek-R, adversarial descriptions induced malicious behavior with successful repair in 51.7% of cases. Detection was weak: LLM-as-judge pre-repair screening reached only 62.3% accuracy, and post-repair detection via static analysis and LLM-as-judge achieved just 39.4% and 55.4%.

arXiv cs.CR·Sep 14, 17:53 UTC · 2d agoAI safety & security2

PaperCut Replaces Emergency Patches With Fixes for Two Actively Exploited Flaws

PaperCut shipped maintenance releases replacing emergency patches for two actively exploited flaws abused in AI-assisted attacks on 395 organizations.

PaperCut released PaperCut NG/MF versions 26.0.5, 25.0.13 and 24.1.10, superseding Emergency Patch Releases 1-3 for CVE-2026-81578 and CVE-2026-82078, which enable authentication bypass and arbitrary code execution on susceptible instances. GreyNoise and Blackpoint Cyber reported a suspected Russian-speaking actor weaponizing both flaws against at least 395 organizations in 48 countries, concentrated in the U.S. education sector. The campaign used hundreds of AI agents powered by OpenAI's Codex harness and a DeepSeek model, originating from IP 45.142.193.132, and avoided organizations in Russia, China, Hong Kong, Thailand and Iran.

The Hacker News·updated Sep 11, 10:05 UTC · 5d ago·first Sep 11, 06:46 UTC · 5d agoExploit / PoC in the wild 7 sourcesCVE-2026-81578CVE-2026-820781

AI Is Ending the Era of Hidden Vulnerabilities — Are Vendors Ready?

Dark Reading argues AI-assisted bug discovery is flooding vendors with vulnerability reports, straining disclosure processes and secure-by-design commitments.

The Dark Reading analysis describes a surge of bug reports driven by AI-powered discovery, exposing bottlenecks in vendor triage and disclosure pipelines. It argues this volume is revealing secure-by-design failures and questions whether vendors can keep pace with the rising tide of findings.

Dark Reading·Sep 4, 13:00 UTC · 12d agoIndustry

CVE-2026-82561: Apache NiFi: Missing Authorization for Components Referenced in Flow Update Methods

Apache NiFi 1.5.0-2.11.0 flow update REST methods lack authorization checks for referenced components, permitting unauthorized Process Group flow replacement (CVE-2026-82561).

Apache NiFi 1.5.0 through 2.11.0 expose REST API methods that replace the entire contents of a Process Group with a client-supplied flow definition, including versioned flow update and rebase operations. Framework authorization for these methods was limited to read and write privileges on the target Process Group, without checking components referenced in the flow (CVE-2026-82561). No severity rating was provided in the disclosure; the affected version range is broad.

oss-security·Sep 16, 15:17 UTC · 13h agoVulnerability 4 sourcesCVE-2026-825611

Fluid Notarization: Verifiable Evolution of Concurrently Edited Structured Documents

Fluid Notarization anchors delta-CRDT change graphs on blockchain, providing verifiable provenance for concurrently edited documents, demonstrated on collaborative electronic health records.

The paper introduces Fluid Notarization, a paradigm that notarizes the evolution of collaboratively edited structured documents rather than isolated snapshots. It builds on Melda, a JSON-native delta-CRDT representing changes as compact content-addressed deltas linked by causal dependencies, with blockchain notarization reduced to recording identifiers of evolution artifacts while synchronization, reconstruction, and conflict resolution remain off-chain. The architecture combines deterministic CRDT convergence with independently auditable proof-of-existence, provenance, and publication evidence, validated through a prototype based on collaboratively edited electronic health records.

arXiv cs.CR·Sep 16, 16:20 UTC · 12h agoResearch