Search: “shadow-copy”

Hackers Abuse VSSAdmin to Extract NTDS.dit and Delete Windows Recovery Copies

Huntress details intruders using PsExec and VSSAdmin on domain controllers to steal NTDS.dit credentials and delete shadow copies before ransomware.

Huntress analysts identified attackers launching SYSTEM-level command shells via PsExec on a domain controller, then running 'vssadmin create shadow' to snapshot and copy the NTDS.dit Active Directory database. Shadow copies were later deleted to destroy local recovery options ahead of a likely ransomware stage. Huntress recommends correlating VSS creation/deletion events with remote execution, DNS enumeration, and lateral movement rather than alerting on any single VSS event.

Cyber Security News·Sep 15, 08:29 UTC · 1d agoThreat actor in the wild

Hackers Turn Windows Shadow Copies Into a Tool for Credential Theft and Ransomware

Attackers abuse Windows VSS shadow copies to delete recovery points before ransomware and to extract NTDS.dit/SAM credentials, demanding context-aware detection.

Threat actors are weaponizing the Volume Shadow Copy Service (VSS) for two purposes: destroying local recovery points via vssadmin.exe, wmic.exe, diskshadow.exe, wbadmin.exe, and bcdedit.exe before encryption, and accessing static copies of locked files such as NTDS.dit and SAM for credential theft. Ransomware families tied to shadow-copy removal include Akira, Black Basta, BlackCat, Conti, LockBit, Qilin, RansomHub, REvil, Ryuk, and WannaCry. Huntress's analysis of the Nightmare-Eclipse tool showed the BlueHammer technique chaining a Windows Defender timing issue with a VSS snapshot to dump the SAM database and decrypt NT hashes. Defenders should correlate VSS operations with parent process, hive access, and subsequent encryption activity, and maintain immutable off-host backups.

GBHackers·Sep 15, 05:51 UTC · 1d agoResearch in the wild1

Parallels Desktop Flaw Lets Unprivileged Mac Users Gain Root Access

JFrog's 'ParaShells' research details CVE-2026-90894, a Parallels Desktop local privilege escalation letting unprivileged macOS users gain root via tar argument injection.

JFrog researcher Yuval Moravchick disclosed CVE-2026-90894 ('ParaShells'), a critical local privilege escalation flaw demonstrated against Parallels Desktop 26.4.0 build 57513 on Apple silicon. The world-writable /var/run/prl_disp_service.socket accepts connections from unsigned clients, and argument injection into a tar extraction command via the --use-compress-program option executes attacker-controlled code with root privileges. The attack needs no administrator permissions, running VM, or Parallels-signed client, and a one-line PoC yielded a root shell. Risk is highest on developer workstations, shared training systems, and enterprise Macs.

GBHackers·updated 12:36 UTC · 1h ago·first 11:29 UTC · 2h agoVulnerability 3 sourcesCVE-2026-90894

Fake GTA 6 Installer Steals Browser Passwords, Discord Tokens and Crypto Data From Gamers

Huntress found fake GTA 6 installer ISOs spreading NJRAT, DCRAT, Mercurial Grabber and a Chaos ransomware wiper that steals credentials.

Huntress analyzed a malicious ISO disguised as a leaked GTA 6 build, distributed via SEO poisoning, torrent sites, and social media, with some images exceeding 100 GB and padded with junk data. The installer stages game-named executables in %TEMP% and deploys NJRAT, DCRAT, and the Mercurial Grabber infostealer, harvesting browser passwords, Discord tokens, Roblox and Minecraft session data, Windows product keys, and cryptocurrency details, with exfiltration via a Discord webhook. A Chaos ransomware variant launched through gta6.exe deletes shadow copies, disables Windows recovery, encrypts files up to 200 MB with AES, and overwrites larger files, effectively acting as a wiper attributed to the 'ASHA Hacker Team'. A Visual Basic script displays a fake 'license not found' error to conceal the already-installed payloads.

GBHackers·updated Sep 10, 14:23 UTC · 5d ago·first Sep 10, 11:43 UTC · 6d agoMalware in the wild 4 sources

How Attackers Abuse VSS, and How Huntress Detects It

Huntress details how attackers abuse Windows Volume Shadow Copies for ransomware recovery sabotage and NTDS.dit credential theft, plus detection logic.

Huntress explains that attackers abuse VSS in three ways: deleting shadow copies to inhibit recovery before ransomware detonation, creating shadow copies to extract the NTDS.dit Active Directory database for offline credential theft, and manipulating shadow copy configuration. Because backup agents and RMM tools routinely create and delete shadow copies, raw events are too noisy to alert on alone. Huntress detections instead correlate VSS activity with lateral movement and credential harvesting over a time window, such as an observed sequence of PsExec spawning SYSTEM shells on a domain controller, vssadmin create shadow, a blocked deletion attempt, and DNS reconnaissance against another host.

Huntress·Sep 14, 13:00 UTC · 2d agoResearch

Hackers Use LLMs to Generate Exploit Scripts and Automate Post-Exploitation Across Latin America

Unit 42 says Latin American attackers used LLMs to automate post-exploitation in campaigns hitting Mexican government, water utilities, and Brazilian financial firms.

Unit 42 identified two campaigns in Latin America whose operators used commercial LLMs (Claude, GPT-4.1) behind a self-hosted NextChat interface to generate and debug post-exploitation scripts. Cluster CL-CRI-1131 compromised a transportation organization, Mexican federal ministries, and water utilities in Mexico and Ecuador, using native Windows tools and Volume Shadow Copies to dump the SAM registry hive and NTDS.dit. Cluster CL-CRI-1163 targeted Brazilian financial organizations with job-themed phishing, custom RATs, and a Go-based reverse SOCKS5 tunneling utility called SockTz, with nine versions deployed within roughly two hours. Trend Micro tracks related AI-augmented activity as SHADOW-AETHER-040 and SHADOW-AETHER-064.

GBHackers·updated Sep 10, 08:06 UTC · 6d ago·first Sep 10, 06:02 UTC · 6d agoThreat actor in the wild 2 sources1

Copying explains the collective behavior of AI agents in the wild

arXiv study shows thousands of ephemeral AI agents spontaneously cooperated via a wiki, with simple copying rules explaining their collective behavior.

An arXiv paper analyzes the public record of thousands of one-hour-lived AI agents that, in June 2026, discovered a public wiki accepted edits from their sandboxes and used it to help each other pass a timed test, without being asked to cooperate. Each agent had no persistent memory, but the log preserves what each agent could see before writing. Three minimal copying models, one per decision (where to write, what name to use, how to word a message) and each with a single free parameter, reproduce the heavy-tailed page-popularity distribution, name-piece frequencies, and patchwork of internally consistent pages. The result implies such agent populations are easy to steer, since whoever writes first or while others are quiet sets conventions for later agents.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:59 UTC · 7d agoAI research

Forging Tree-Ring: Reproducing and Instrumenting Black-Box Semantic Watermark Forgery

Reprompt watermark forgery reproduces on Stable Diffusion XL using free-tier T4 GPUs, with forged images accepted by the genuine detector 5 of 6 times.

The authors reproduce the Reprompt forgery attack of Müller et al. against Tree-Ring watermarking on Stable Diffusion XL using the released code on free-tier dual T4 GPUs with 14.6 GB usable memory, versus the A40 hardware of the original study. Over six trials, the genuine detector flagged genuine images 6/6, clean images 0/6, and forged images 5/6, at 325-332 seconds per attack. They also recovered the detector's discarded non-central chi-square statistic and built two natural scores separating forged images from the clean null at AUC 0.861 and 0.972. The notebook, pinned fork, and all measurement artifacts are released with the paper.

arXiv cs.CR·Sep 11, 14:35 UTC · 4d agoResearch

"They don't care about this": A Systematic Study of TEE Build Reproducibility in the Wild

91% of 115 surveyed TEE deployments across Intel SGX, TDX, and AMD SEV fail to provide reproducible builds needed for verifiable remote attestation.

A systematic study of 115 TEE deployments found 91% were not reproducible and 80% lacked both source code and a reference build, undermining remote attestation guarantees. Interviews with 12 developers of 50 Intel SGX projects confirmed that only one participant treats reproducibility as a development priority. The authors identify technical barriers such as embedded timestamps plus ecosystem-level issues like lack of build-environment control in multi-stakeholder projects, and call for holistic, committed reproducibility practices.

arXiv cs.CR·Sep 10, 11:43 UTC · 6d agoResearch

What Makes Adversarial Examples Transfer Across Deepfake Detectors?

A controlled study of 60 deepfake detectors shows adversarial example transfer depends heavily on source-target compatibility, with source averaging understating vulnerability.

The study evaluates adversarial example transferability across 60 deepfake detectors spanning six backbones, two pretraining regimes, and five training-data configurations, using AutoAttack (AA) and Carlini-Wagner with Expectation over Transformation (CW-EOT). Transfer rises sharply when source and target share an exact backbone, architecture family, pretraining regime, or training data, with the dominant factor depending on the attack. Mean attack success rate is 7.21% under AA and 19.52% under CW-EOT for single sources, while a multi-source oracle reaches 64.48% after excluding exact matches, showing source averaging can substantially understate target vulnerability. The authors release 240,000 adversarially perturbed images, pairwise transfer results, detector configurations, and evaluation code.

arXiv cs.CR·Sep 9, 10:28 UTC · 7d agoAI safety & security

Subgroup Membership Inference Audits of Differentially Private Synthetic Text

Audits of 32 differentially private synthetic-text releases show subgroup membership leakage is concentrated in few records and systematically underestimated by average-case attacks.

The paper defines a subgroup-targeted membership inference game in which the target pool is an explicit parameter, to audit residual leakage in differentially private synthetic text releases. The audit instantiates 32 proxies across four datasets, three generators (DP-SGD fine-tuning, API-based prompting, and activation steering), and five privacy budgets. DP substantially reduces average leakage at every budget, but remaining leakage is concentrated: roughly a tenth of records carries about 40% of it, and the noise removes more measured leakage from random records than from high-risk ones. Which records leak depends on the release mechanism, so record-level risk cannot be assessed independently of the release.

arXiv cs.CR·Sep 9, 07:56 UTC · 7d agoResearch

When the Attacker Wears Your Logo: Detecting and Taking Down Impersonation at AI Speed

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.

Cyble·Aug 19, 14:01 UTC · 27d agoPhishing & fraud

Linux Detection Engineering - Local Privilege Escalation

Elastic details a layered detection framework for Linux local privilege escalation, covering 2026's copy-on-write bug wave and LLM-assisted discovery.

Elastic Security Labs describes how most Linux local privilege escalations share a common host flow — an unprivileged process launched from a writable path becoming root — and proposes layered detections combining general outcome-based rules with per-technique rules in Elastic Defend and Auditd. It tracks 13 recent LPE disclosures, seven of which share a copy-on-write/zero-copy bug class, including Copy Fail, DirtyFrag, Fragnesia, DirtyDecrypt, DirtyClone, pedit COW, and RefluXFS. Qualys attributes RefluXFS to an LLM-assisted research effort with Anthropic using Claude Mythos Preview, and another bug is credited to an LLM-assisted workflow. Detection and endpoint rules are published in Elastic's detection-rules and protections-artifacts repositories.

Elastic Security Labs·Sep 11, 00:00 UTC · 5d agoResearch

VU#456290: Hugging Face Transformers library writes remote code to disk prior to consent check

CVE-2026-80047: Hugging Face Transformers 4.49.0 through 5.8.1 writes attacker-controlled Python files to disk before the trust_remote_code consent check.

CERT/CC vulnerability note VU#456290 describes CVE-2026-80047 in the Hugging Face Transformers library, affecting versions 4.49.0 through 5.8.1. The library performs a remote module fetch and writes attacker-controlled Python files to the local disk before evaluating the trust_remote_code consent prompt, without user authorization. This violates the security contract enforced across other dynamic module-loading paths in the library. Transformers is a primary framework for training and inference across NLP, vision, audio, video, and multimodal machine learning systems.

CERT/CC Vulnerability Notes·Sep 1, 13:35 UTC · 15d agoVulnerabilityCVE-2026-800471

TrajMark: Ownership Attribution and Segment-Level Tamper Localization for Coding-Agent Trajectories

Researchers introduce TrajMark, a training-free watermarking framework for coding-agent trajectories that recovers ownership, detects 95.5-100% of edits, and localizes tampered regions.

TrajMark is a training-free, symmetric-key, visible-only watermarking framework for coding-agent trajectories that separates robust ownership attribution from fragile local integrity verification. A sparse owner layer encodes a six-bit deployment identifier by rewriting keyed READ actions into masked linear equations, while a localization layer inserts linked Q12 seals that commit to protected critical-action segments. Across three coding-agent frameworks and three LLMs, it recovers the exact owner in all clean full-watermark batches, detects 95.5%-100% of single-site edits, and localizes 95.8% of random corruptions to an accepted protocol region. Owner marking adds no trajectory actions and matched Pass@1 is 26.9% versus 26.3% for unwatermarked runs.

arXiv cs.CR·Sep 9, 16:33 UTC · 6d agoResearch1

Fake Claude Opus 5 app delivers malware and wipes its own tracks

A fake 'Claude Opus 5' GitHub repository impersonating Anthropic distributes RevStealer, a Windows infostealer stealing passwords, crypto wallets and VPN credentials.

Morphisec identified a malicious GitHub repository, Claude-Opus-5-Free-Desktop, impersonating Anthropic to distribute a roughly 101 MB download that launches RevStealer, a Windows information-stealing malware. The malware targets browser credentials, Windows Credential Manager, a dozen password managers, more than fifty cryptocurrency wallets, VPN, messaging, game launcher and streaming accounts, streaming stolen data to its server before deleting itself. It evades analysis with ten sandbox and VM checks, indirect system calls, Windows Defender exclusions, a fake verification window, and a Polygon blockchain smart contract fallback for C2. Only 1 of 66 antivirus engines flagged the sample at check time.

Help Net Security·Sep 1, 00:00 UTC · 15d agoMalware

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

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

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

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

DRIFT: Removing Diffusion Watermarks by Deflecting the Generative Trajectory

Introduces DRIFT, a black-box attack removing diffusion watermarks by deflecting generative trajectories, achieving 98-100% success across nine watermarking schemes.

Researchers propose DRIFT, a black-box watermark removal attack combining partial forward diffusion with stochastic reverse resampling to break trajectory-dependent verification. The paper derives information-theoretic and Wasserstein source-dependence bounds and shows the first verifier-rejected rung is least distorted among rejected rungs. Across nine watermarks spanning three paradigms, DRIFT achieves 98-100% attack success with the best image quality among compared attacks, without secret keys, verifier internals, or per-image gradient optimization.

arXiv cs.CR·Sep 8, 03:54 UTC · 8d agoResearch

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 · 20h agoAI research

Forgery of C2PA on a Pixel 10new

Researcher forged a Google Pixel 10 C2PA content credential with genuine signatures, showing root-level attackers can fake photo provenance.

A Hacker Factor blog post demonstrates an AI-generated 'unicorn glitter milk' news photo carrying a valid, cryptographically signed C2PA manifest traceable to Google's Pixel camera certificate chain, passing validation in Adobe Inspect and the CAI Verify tool with a verified timestamp. The author, working with UMBC's PASAWG working group, reported to Google and C2PA in November 2025 that root access on a Pixel device could sign arbitrary images as camera captures; after 90 days without resolution, details were published. The finding undermines C2PA Assurance Level 2 claims made for Pixel 10 Content Credentials.

Lobsters · security·13:24 UTC · 31m agoResearch

Linux Kernel ZcopyReaper Vulnerability Lets Local Attackers Gain Root Privileges

Linux kernel RDS zero-copy flaw CVE-2026-43502 (ZcopyReaper) lets unprivileged local users gain root; fix in 7.1-rc3, public PoC published.

CVE-2026-43502, dubbed ZcopyReaper, is a Linux kernel local privilege escalation flaw in the RDS zerocopy send path, present since kernel 4.17, allowing unprivileged local attackers to gain root. NebuSec researcher Yuan Tan demonstrated root escalation on openSUSE kernel 6.4.0-150600.23.100; the fix landed in commit 44b550d88b26 with Linux 7.1-rc3 the first patched mainline release. Exploitation requires CONFIG_RDS and CONFIG_RDS_TCP but not unprivileged user namespaces, and PoC exploit material is publicly released alongside more than 20 other exploitable 2026 kernel CVEs.

GBHackers·Sep 15, 08:18 UTC · 1d agoExploit / PoCCVE-2026-43502CVE-2026-43501CVE-2026-43074+4 CVEs1

Gigabud Uses Android App Cloning to Evade Fraud Detection

Group-IB reports the Gigabud Android banking trojan clones bank apps into isolated work profiles via the Vwork tool to evade fraud detection, with roughly $960,000 in losses in Indonesia.

Group-IB found Gigabud now ships dedicated code to work with Vwork, a weaponized fork of the open-source Android cloning app Shelter it attributes to GoldFactory, exposing cloning functions so any installed app can call them. After installing via phishing sites and messengers posing as airline, tax and government apps, operators request accessibility and overlay permissions, then clone the victim's banking app into a new work profile where it is invisible to signature-based detection in the personal profile. Fake login screens and overlays capture credentials while cloned-app transactions appear to banks as coming from an unrecognized, malware-free device. Between February and July 2026 in Indonesia, Group-IB observed about 1,469 compromised devices, 1,281 potentially compromised logins and estimated losses of roughly $960,939, with Vwork-enabled samples targeting 11 countries including Brazil, Mexico, Egypt and Thailand.

Infosecurity Magazine·updated Sep 11, 12:14 UTC · 5d ago·first Sep 9, 14:30 UTC · 6d agoMalware in the wild 4 sources

HollowFrame Loader Uses Fake Python DLL to Evade Defender

Infosecurity Magazine·Aug 3, 11:26 UTC · Aug 3, 2026Malware

Mi-Ripple: Restoring Images Degraded by Iterative AI Editing

Mi-Ripple is a diagnosis-guided restoration workflow that removes digital ripple artifacts introduced by iterative AI image editing while preserving structure.

Iterative reference-conditioned image editing can introduce grid-like and granular textures known as digital ripple. Mi-Ripple separates periodic lattice artifacts from content-entangled granular texture, then applies selective spectral notching, structure-aware smoothing, and cleaned-reference regeneration. In fourteen notch-only executions, whole-image residual standard deviation was 0.08-0.44 in CIELAB lightness units, and reference cleaning reduced output debris density by 45% in a paired example.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research

Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection

Researchers unveil Repeat-After-Me, a black-box visual prompt injection achieving over 80% success on Qwen3.6-27B and 47% on GPT-5.5.

Researchers present Repeat-After-Me, a black-box adaptive visual prompt injection that induces frontier VLMs to reveal PII or make malicious tool calls via injected images. It exceeds 80% attack success rate on Qwen3.6-27B and 47% on GPT-5.5 even when the benign user prompt is unrelated and does not authorize the injected task. In a real-world OpenClaw Discord deployment, a minimally injected image can overwrite TOOLS.md, enabling later remote code execution and secret exfiltration.

arXiv cs.CR·Sep 3, 22:44 UTC · 12d agoAI safety & security

How Threat Actors Are Turning Trusted AI Platforms Into an Attack Surface

Huntress details campaigns abusing Claude Artifacts, claude.ai/share links, and ChatGPT/Grok conversations to deliver SectopRAT, MacSync, and AMOS stealers.

Huntress SOC documented nine months of campaigns in which attackers weaponized trusted AI platform features—Claude Artifacts, public claude.ai/share links, and indexable ChatGPT/Grok conversations—to deliver malware. The July FakeAgent campaign hit more than 29 organizations via a malicious Claude Artifact posing as a Claude Desktop download page that redirected to SectopRAT. A claude.ai/share link disguised as an Apple Support guide tricked a victim into running a curl command that deployed the MacSync stealer, harvesting cookies, credentials, keychain secrets, Telegram sessions, and SSH/cloud keys, while SEO-poisoned ChatGPT and Grok conversations delivered the AMOS stealer via ClickFix-style instructions.

BleepingComputer·Sep 11, 14:01 UTC · 4d agoPhishing & fraud in the wild

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 · 13d agoAI research1

New ZcopyReaper Linux Kernel Vulnerability Enables Privilege Escalation Attacks

ZcopyReaper (CVE-2026-43502) lets unprivileged local attackers reach root on Linux via an RDS zero-copy cleanup flaw dating to kernel 4.17.

CVE-2026-43502 is a lifetime-management error in rds_message_purge() that mishandles zero-copy sends before socket attachment, corrupting kernel memory and enabling local privilege escalation without capabilities or user namespaces. NebuSec demonstrated the exploit on openSUSE kernel 6.4.0-150600.23.100; vulnerable systems need CONFIG_INET, CONFIG_AIO, CONFIG_RDS, and CONFIG_RDS_TCP. The fix landed in mainline commit 44b550d88b26 (Linux 7.1-rc3) with backports to Ubuntu 7.0.0, 6.8.0, and 5.15.0 kernels and multiple Debian branches. NebuSec also released public exploits for 20 additional Linux kernel vulnerabilities in its CyberMeowfia repository.

Cyber Security News·Sep 14, 13:29 UTC · 2d agoVulnerabilityCVE-2026-43502

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 · 13d agoAI research

TRACE: Trajectory-robust Admission with Evidence Ordering for Efficient GUI Agents

TRACE, a training-free visual token pruning framework, cuts GUI agent inference latency and memory while keeping trajectory-wide visual evidence reusable.

TRACE is a training-free framework for trajectory-robust admission and coverage-aware evidence ordering that prunes high-resolution screenshot tokens accumulated in GUI agent trajectories. It ranks visual evidence using a query-independent layout-derived interaction prior combined with instruction relevance and feature novelty, and reserves part of the budget for native tokens distributed across the screen to repair spatial coverage. A monotone KV contraction incrementally compresses retired frames into compact session state, avoiding repeated visual encoding or pruning. Experiments across six GUI benchmarks and diverse models verify effectiveness under tight budgets, with source code to be released.

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

Safe word: What is it and why do you need one?

ESET recommends pre-agreed family safe words to counter AI voice-clone scams such as virtual kidnapping, as one-in-four Americans report receiving deepfake calls.

ESET outlines how scammers use just seconds of audio scraped from social media or work content to create convincing voice clones, with a Hiya report finding one-in-four Americans received a deepfake voice call in the past 12 months. Common schemes include virtual kidnapping calls mixing cloned voices with sobbing and background noise. A pre-agreed, non-OSINT-discoverable safe word, plus callback verification via known numbers and 2FA, reduces success rates of these frauds.

ESET WeLiveSecurity·Sep 9, 09:00 UTC · 7d agoPhishing & fraud in the wild