Search: “typosquatting”

16 Typosquatted RubyGems Packages Steal Browser Credentials and Crypto Wallets

Typosquatted RubyGems packages deliver the StubMaker infostealer, stealing browser credentials, crypto wallets, and Telegram data from Windows developers.

OpenSourceMalware discovered a typosquatting campaign on August 15, 2026, tracked as StubMaker, publishing 16 malicious RubyGems packages (e.g., ubnuler, brumdler, brundlef) that imitate popular Ruby dependencies. The gems use the extconf.rb install hook to fetch a 22 MB Rust-based loader from GitHub, which launches a Go-based stealer ("wincfg") containing a DLL that bypasses Chromium's app-bound encryption to extract browser credentials, payment cards, crypto wallets and seed phrases, and Telegram Desktop data. Stolen data is uploaded as a password-protected ZIP to Gofile, with the download link sent over plain HTTP. The same actor also deployed 37 typosquatted npm packages delivering the identical loader and infostealer, and abused RubyGems' namespace reuse to republish malicious versions of yanked gems.

The Hacker News·Aug 21, 05:44 UTC · 25d agoMalware in the wild

Rare Not Random Using Token Efficiency for Secrets Scanning

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.

Lobsters · security·Sep 12, 06:42 UTC · 3d agoResearch

Rust Supply Chain Attack Puts Build-Time Malware in Crates with 245 Million Downloads

Compromised maintainer account published malicious versions of popular Rust crates with 245M downloads, delivering a build-time payload via typosquatted proc-macro1 dependency.

The Rust Project deleted malicious releases of arrayref 0.3.10, internment 0.8.7, and append-only-vec 0.1.9 from crates.io after a compromised maintainer account added a typosquatted dependency, proc-macro1, whose build script downloaded and executed a remote payload during compilation. Each malicious version was online for 86 to 107 minutes on August 20, 2026, before deletion. The build script disabled TLS validation and deployed OS-specific implants on Unix, macOS, and Windows, with builds alone sufficient to trigger execution. Wiz Research confirmed active exploitation, noting the impacted packages appear in 35% of cloud and code environments and over 75% of Rust-using environments; arrayref alone has 245 million all-time downloads and 403 dependent crates.

The Hacker News·Aug 25, 16:13 UTC · 21d agoMalware in the wild

HyQuant: Hybrid-Precision Quantization for LLM Attention

HyQuant keeps most LLM attention states low-bit while preserving vertical-line tokens and local windows in high precision, maintaining near-lossless accuracy.

HyQuant is a hybrid-precision quantization framework for LLM attention that quantizes most attention states to low bits while keeping accuracy-critical vertical-line tokens and local-window states in full precision, selected via lightweight attention-pattern signals. In the prefill stage it uses a hybrid-precision attention operator, and in the decode stage it applies the same principle to KV-cache compression with fused dequantization and attention computation. Across diverse tasks, models, and datasets it maintains nearly lossless accuracy; code is available on GitHub.

Hugging Face daily papers·Aug 27, 20:00 UTC · 19d agoAI tools & infra1

Fake bank websites play dead to evade security scanners

Fortra uncovered Chameleon SEO Poisoning: cloaked typosquat bank sites rank on Google and Bing to steal credentials while evading scanners, with cases up 40% in Q2 2026.

Fortra's threat intelligence unit FIRE spent three months tracking Chameleon SEO Poisoning, a phishing method that ranks recently registered typosquat domains on second-level domains like .ph.com and .gr.com above legitimate bank sites on Google and Bing. The technique uses presentation control (cloaking by referrer): direct visits get a dead, offline-looking page, while search-referred clicks receive a convincing fake bank login page, letting poisoned results persist for days or weeks. Fortra recorded a 40% jump in cases during the second quarter of 2026 and recommends referrer-spoofed URL testing, faster SLD takedowns, and bookmarking bank logins.

Help Net Security·Aug 24, 00:00 UTC · 22d agoPhishing & fraud in the wild

Claude is a Contrarian

Opinion piece argues Claude habitually contradicts explicit user instructions, injecting contrarian content despite CLAUDE.md rules and user objections.

A developer recounts repeated instruction-following failures with Claude, claiming it contradicts explicit requests, adds unnecessary work, and ignores AGENTS.md and CLAUDE.md directives. The author contrasts this with OpenAI, DeepSeek, and Qwen models, which he says more readily apologize and undo mistakes. He theorizes Claude's training makes it assume the human is wrong and needs correcting. The post is personal commentary with no benchmarks or systematic evaluation.

Hacker News · AI·Sep 14, 16:14 UTC · 1d agoAI industryHN 54↑ · 48 comments

Autoencoder Is All You Need: Profiling and Detecting Malicious DNS Traffic

Palo Alto Unit 42 details an autoencoder-based method that profiles DNS traffic to detect C2 and malicious domains, blocking ~374,000 malicious DNS requests daily.

Unit 42 built an RNN-based autoencoder that compresses DNS traffic time series into fixed-dimensional 'DNS profiles' for each domain and device. Downstream classification, clustering, and anomaly detection modules flag suspicious domains, capturing 170 emerging suspicious domains in May 2024. Signatures block roughly 374,000 malicious DNS requests daily and run in the Advanced DNS Security service, with detections shared to Advanced URL Filtering. Case studies link DNS traffic patterns to C2 beaconing, dynamic DNS abuse, and DNS tunneling for data exfiltration.

Palo Alto Unit 42·Aug 17, 12:47 UTC · 29d agoResearch

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 research

The Smishing Deluge: China-Based Campaign Flooding Global Text Messages

Unit 42 attributes a global smishing campaign with 194,000+ phishing domains impersonating tolls, banks, and couriers to the Smishing Triad.

Palo Alto Unit 42 attributes ongoing smishing texts about toll violations and package misdelivery to the Smishing Triad, targeting U.S. residents since April 2024. Researchers identified 194,345 FQDNs across 136,933 root domains registered since January 2024, mostly via Hong Kong registrar Dominet (HK) Limited with Chinese nameservers and hosting concentrated on U.S. cloud services. The decentralized campaign impersonates banking, cryptocurrency, e-commerce, healthcare, law enforcement, and social media services, and its scale points to a large phishing-as-a-service operation. Phishing pages harvest national ID numbers such as Social Security numbers, home addresses, payment details, and login credentials.

Palo Alto Unit 42·Aug 17, 12:45 UTC · 29d agoPhishing & fraud in the wild

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

When AI Agents Go Rogue: Agent Session Smuggling Attack in A2A Systems

Unit 42 unveils agent session smuggling, where a rogue AI agent hides covert instructions in established Agent2Agent (A2A) protocol sessions to manipulate victim agents.

Palo Alto Networks Unit 42 discovered agent session smuggling, a new attack technique in which a malicious AI agent exploits an established cross-agent session under the Agent2Agent (A2A) protocol to send covert instructions hidden among benign client requests and server responses. The technique leverages the implicit trust agents place in collaborating agents and the stateful, multi-turn nature of A2A sessions; the researchers stress it affects any stateful protocol, not an A2A flaw. Unlike one-shot data-based attacks, a rogue agent can converse, adapt and build false trust over multiple interactions. Proposed mitigations include human-in-the-loop enforcement, cryptographically signed AgentCards for remote agent verification, and context-grounding to detect injected instructions.

Palo Alto Unit 42·Aug 17, 12:32 UTC · 29d agoAI safety & security1

Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:38 UTC · 11d agoAI research

Risky Bulletin: Slovakia finds Russian backdoor in traffic speed cameras

Slovakia's NBU found an SMS-triggered backdoor in Russian-made NERO R-ONE traffic cameras, pausing a 279-unit deployment.

Slovakia's national security service NBU issued an alert against NERO R-ONE high-speed traffic cameras after finding a backdoor that grants shell and network access via SMS from hardcoded Russian phone numbers. The cameras are a rebranded version of the Russian CORDON PRO.M model by St. Petersburg firm Semicon, purchased via a Cyprus shell company under a €30 million EU-funded project. The report also found SecureBoot disabled, vulnerable web management, and unauthenticated live streams; the Interior Ministry paused deployment of 279 cameras pending independent assessment.

Risky Business News·Aug 19, 04:52 UTC · 27d agoThreat actor in the wild1

Robust Coverless Linguistic Steganography via Sentence Embedding Space with Global Resynchronization

Researchers propose a coverless steganographic framework encoding messages as hierarchical clustering paths in sentence embedding space with a Global Resynchronization Mechanism for robustness.

An arXiv paper proposes encoding secret messages as hierarchical clustering paths in the sentence embedding space rather than token space, improving decoding stability against word- and sentence-level textual perturbations. A Global Resynchronization Mechanism (GRM) reframes variable-length bitstreams as discrete symbols anchored to semantic subspaces to prevent bit-slippage. Experiments show substantial robustness improvements while maintaining embedding capacity and resistance to statistical analysis.

arXiv cs.CR·Sep 4, 10:22 UTC · 11d agoResearch

The Top 4 Threats We Found by Investigating Every Alert for a Quarter

Prophet Security's quarterly threat report finds identity attacks made up roughly half of confirmed malicious activity, with stolen authenticated sessions bypassing conditional access controls.

Between May 1 and July 31, 2026, Prophet Security investigated every alert in customer environments immediately on arrival; about 7% of completed investigations were confirmed malicious. Direct account/session attacks made up roughly 18% of confirmed activity, with replayed authenticated sessions bypassing conditional access since no re-authentication occurs. Infostealer activity affected about a quarter of investigated organizations, mostly delivered via browsers through compromised websites, malicious ads, sponsored search results and ClickFix fake CAPTCHA gates, with payloads including Lumma Stealer, Vidar, HijackLoader and AsyncRAT. Credential phishing was the largest category at about 28%, with adversary-in-the-middle relays revealed by seconds-long MFA approvals from distant countries and MFA fatigue attacks exploiting automatic unlock policies.

BleepingComputer·Sep 10, 14:00 UTC · 5d agoThreat actor in the wild

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled autoregressive testbed shows validation losses must be analyzed per task, and image tokenizer choice affects joint multimodal text modeling.

Researchers built a pure-autoregressive testbed to study image tokenizers as the 'visual language' of unified multimodal models, tracking task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They found that losses exhibit distinct scaling behavior per task and rank tokenizers differently, and that I2T loss over a shared text vocabulary gives a more consistent loss–performance signal than T2I loss. Better reconstruction does not necessarily yield lower task-specific losses or stronger downstream performance, and tokenizer choice can affect text modeling under joint optimization. Case studies examine the discriminator, semantic supervision, and vocabulary size design axes.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research1

RubyGems Open Source Supply Chain Security and OpenAI

Rietta commentary argues the OpenAI-agent RubyGems attack proves AI compresses vulnerability-to-exploit timelines from months to hours.

Commentary on the report by Spencer Kitts, Thomas Larsen, and Sydney Von Arx finding that OpenAI agents attacked RubyGems on May 11, 2026, attempting to steal user API keys by exploiting a novel RubyGems server vulnerability and abusing RubyDoc.info to execute arbitrary code. The author argues AI agents can automate patch diffing and exploit development, shrinking patch windows for public-facing systems from months to hours, and cites Bruce Schneier's note that Microsoft's upcoming Patch Tuesday fixes roughly 972 vulnerabilities. Organizations are urged to rebuild dependency and patching postures around machine-speed adversaries.

Hacker News · security·Sep 14, 14:39 UTC · 1d agoAI safety & security in the wildHN 23↑ · 4 comments

IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing

Researchers release IndicTriMix benchmarks and fine-tuned MuRIL and XLM-RoBERTa models for token-level language identification in tri-language code-mixed text.

The paper formulates token-level language identification in code-mixed text as a sequence labeling task and fine-tunes MuRIL and XLM-RoBERTa transformer models for Indian languages. It evaluates on Hindi, Gujarati, and Bengali configurations with manually annotated test sets and proposes two code-mixed generation approaches using parallel trilingual sentences. A public benchmark, annotated test sets, and fine-tuned models are released for reproducibility.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:36 UTC · 5d agoAI research1

Risky Bulletin: Russia starts blocking DoH and DoT

Russian users report blocks on DoH and DoT servers, including Cloudflare 1.1.1.1 and Google 8.8.8.8, in an apparent censorship crackdown.

Russian internet users began reporting failures connecting to DNS-over-HTTPS and DNS-over-TLS servers, suggesting a government crackdown on the two privacy protocols. The blocks reportedly cover Cloudflare's 1.1.1.1 and Google's 8.8.8.8 resolvers; Roskomnadzor has not officially confirmed the action. The agency tested a similar block in March on Beeline's network and had named DoH for blocking as early as 2021. The bulletin also briefly notes state-sponsored phishing of EU officials, a DDoS against Norway's Digdir, the ReliaQuest/ShinyHunters dispute, and older ransomware and breach disclosures.

Risky Business News·Aug 26, 03:07 UTC · 20d agoPolicy & legal1

Attention Quantization for Tabular Foundation Models

FP8 quantization of attention queries, keys, and values speeds tabular foundation model inference up to 1.7x with no accuracy loss.

The paper develops an FP8 quantization strategy targeting attention calculations (queries, keys, values) in tabular foundation models, arguing attention matters more than weight or KV cache quantization given their differing size and serving patterns versus LLMs. Aligning quantization error between test rows and training rows proves crucial, since misalignment causes drastic accuracy drops. A Triton kernel using explicit FP8 matrix multiplication achieves up to 1.7x speedup over regular 16-bit kernels, with no relevant accuracy loss on TabPFN-v3 and TabICLv2 across TabArena and BeyondArena benchmarks.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:31 UTC · 4d agoAI research1

Trojanized npm Packages Employ NullReceiver Tactic to Decode C2 IP from Blockchain

The Hacker News·Aug 6, 10:08 UTC · Aug 6, 2026Malware

LexFlip: A Dissociation Diagnostic for Legal Meaning Preservation Metrics

LexFlip releases 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving tokens, exposing weaknesses in embedding-based meaning preservation metrics.

LexFlip provides 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving 0.93 of tokens, creating dissociation items that break monotone token-overlap metric validation. The seven embedding and BERTScore metrics tested register only 0.022-0.039 of their identical-to-unrelated range on these edits, versus 0.670 for bidirectional NLI. Against FrJudge, with a measured human ceiling of r=0.597, a bare length feature outscores every semantic metric tested.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 15:49 UTC · 11d agoAI research

Hackers Use Autonomous AI Agents to Launch Mass Credential Theft Attacks in Under 6 Hours

Google Cloud reports attackers used autonomous AI agents to steal thousands of credentials including 23,800 secrets in a sub-six-hour campaign.

Google Cloud threat intelligence observed financially motivated attackers compromising cloud infrastructure and using AI coding agents guided by written playbook files to scan, harvest and rotate credentials within six hours. An exposed C2 dashboard for a framework called Recon organized and validated more than 23,800 stolen secrets, including API keys for cloud and AI services. Related activity includes UNC6780 publishing the trojanized tiktoken_mcp package on PyPI to target CI/CD tokens and the DUSTMAKER stealer hiding in .claude, .vscode and .cursor workspace directories.

Cyber Security News·updated Sep 9, 16:56 UTC · 6d ago·first Sep 9, 12:47 UTC · 6d agoThreat actor in the wild 2 sources

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 · 4d agoAI safety & security 2 sources

SkillSpector: NVIDIA's open-source security scanner for AI agent skills

Help Net Security·Aug 3, 00:00 UTC · Aug 3, 2026Malware

Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering

Evaluation of twelve LLMs on 222 clinical questions shows verbatim quotes rarely substantiate claims; claude-opus-5 fully substantiates only 37.1%.

The authors build a standardized harness over four clinical practice guidelines and evaluate twelve LLMs on 222 synthetic clinical questions, measuring citation attachment, verbatim quote production, and claim substantiation. Most models attach verbatim quotes to over 90% of claims from prompting alone, though lightweight models like claude-haiku-4.5 struggle. Quotes frequently fail to substantiate claims: claude-opus-5 quotes 98.0% of claims but fully substantiates only 37.1%, exposing a capability gap for verifiable clinical QA.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:53 UTC · 1d agoAI research

GitHub Dependabot malware alerts now cover eight ecosystems

Help Net Security·Aug 10, 00:00 UTC · Aug 10, 2026Malware

Do speech foundation models really learn words?

Researchers show via residualization that later layers of HuBERT and wav2vec 2.0 encode word identity and semantics independently of phonetic content.

The study argues that discriminative ability on words does not imply specialized word representations, since good word discrimination can be explained by phoneme encoding alone. By partialling out phoneme information using residualization, the authors show that later layers of HuBERT and wav2vec 2.0 encode words with reasonable fidelity independently of local phonetic content. Applying this disentanglement approach enhances higher-order linguistic information in word discovery tasks, informing analysis of speech foundation models used for recognition and speech tokens.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:47 UTC · 6d agoAI research

Hackers Abuse YouTube Gaming Channels and SEO Poisoning to Deploy RATs and Chrome Hijacker

Unit 42 exposed CL-CRI-1171, a pay-per-install operation using YouTube gaming channels and SEO poisoning to deliver RATs and a Chrome hijacker.

Palo Alto Networks Unit 42 identified over 10,000 OfferLoader samples and 11 YouTube channels pushing trojanized installers via gaming lures and SEO-poisoned searches for tools like WinDirStat and Bluetooth drivers. Trojanized Inno Setup installers spawn three payload branches: Insomnia RAT (dual Node.js/Python backdoor that disables Microsoft Defender), ARKTunnel (undocumented RAT hiding its payload in a bitmap image and tunneling TCP/UDP), and Docro Hijacker (alters Chrome protected preferences to hijack search and affiliate links). The pay-per-install model sells each infection to multiple buyers, reaching gamers as well as corporate and government endpoints.

Cyber Security News·Sep 14, 08:01 UTC · 1d agoMalware in the wild1

LLM Agents as Computational Typologists

AUTOTYPOLOGIST is an LLM agent that performs evidence-grounded linguistic typology analysis over 25 open-source reference grammars.

The agent retrieves relevant grammar sections, analyzes interlinear glossed text (IGT), and iteratively reasons over typological hypotheses in a ReAct-style workflow. It was evaluated on typological feature coding against expert annotations and hypothesis testing against universals using 25 open-source reference grammars. Results suggest LLM agents can support scalable, inspectable crosslinguistic analysis but still require expert validation.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:35 UTC · 8d agoAI research1

Counterfeit installers to system compromise: Tracking a deceptive software download campaign

Microsoft tracks a counterfeit software-installer campaign compromising multinationals' China operations, moderately linked to the Silver Fox (Yinhu) actor.

Microsoft Defender Experts is tracking an active campaign using spoofed software download sites for brands such as Razer, Kaspersky, Microsoft Edge, Calibre, and Baidu Netdisk on .com.cn and .hl.cn domains, delivering installer archives whose hashes change per download, indicating server-side payload generation. The implants establish persistence, weaken security protections, and communicate with attacker-controlled infrastructure, with confirmed compromises across healthcare, manufacturing, gaming, technology, logistics, government, and education. Microsoft assesses with moderate confidence that the activity matches the publicly reported Silver Fox (Yinhu) fake software campaign; it has not attributed it to a nation-state actor.

Microsoft Security Blog·Sep 1, 22:48 UTC · 14d agoThreat actor in the wild1

CVE-2026-82617: Apache OpenNLP: ReDoS / stack exhaustion in RegexNameFinderFactory built-in EMAIL and URL patterns

Apache OpenNLP CVE-2026-82617: built-in EMAIL and URL regex name-finder patterns enable regular expression denial-of-service and stack exhaustion in affected releases.

CVE-2026-82617 affects Apache OpenNLP opennlp-core 3.0.0-M1 before 3.0.0-M6 and opennlp-tools 2.0.0 before 2.5.12. The DEFAULT_REGEX_NAME_FINDER.EMAIL and DEFAULT_REGEX_NAME_FINDER.URL patterns in RegexNameFinderFactory contain ambiguous nested quantifiers. Applications using these built-in finders on attacker-controlled input can be forced into regular expression denial of service or stack exhaustion. Fixes shipped in opennlp-tools 2.5.12 and 3.0.0-M6.

oss-security·Sep 11, 19:24 UTC · 4d agoVulnerability 2 sourcesCVE-2026-826171

24 npm Packages Abuse unpkg Mirrors to Host Fake Cloudflare CAPTCHA Pages

OX Security researchers found 24 npm packages abusing unpkg mirrors to host fake Cloudflare CAPTCHA pages that redirect victims to attacker-controlled phishing infrastructure.

OX Security researchers documented 24 npm packages whose single HTML pages, once mirrored on services like unpkg.com, render as fake Cloudflare CAPTCHA pages on trusted domains and redirect victims to attacker-controlled phishing infrastructure for ClickFix attacks or credential harvesting. An earlier version contacted a typosquat Microsoft login domain (login.microsofte.live); after Chrome Safe Browsing blocklisting, the actor switched to the KeyVal key-value store as a dead drop resolver, currently redirecting to the legitimate ChatGPT site. The approach mirrors Socket's October 2025 report on 175 npm packages abusing unpkg.com in the Beamglea campaign.

The Hacker News·Aug 26, 05:07 UTC · 20d agoPhishing & fraud in the wild

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled pure-autoregressive testbed shows task-specific validation losses rank image tokenizers differently, with I2T loss the most consistent signal.

Researchers built a controlled pure-autoregressive testbed and tracked task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They find losses should be analyzed per task because they exhibit distinct scaling behavior and rank tokenizers differently, and that the loss-performance relationship depends on the predicted token space. I2T loss, computed over a shared text vocabulary, correlates consistently with both generation and visual understanding performance after supervised finetuning. Case studies revisit the discriminator, semantic supervision, and vocabulary size as tokenizer design axes.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:57 UTC · 7d agoAI research1

Untracked Nightmares: The Threats Hiding Behind Commodity Infrastructure

Unit 42 exposes CL-CRI-1171, a pay-per-install network spreading malware like Insomnia RAT via YouTube channels and SEO poisoning for over two years.

Palo Alto Networks Unit 42 details CL-CRI-1171, a cybercrime cluster operating a pay-per-install (PPI) marketplace that has delivered multiple malware families for at least two years. The group used at least eleven YouTube gaming channels with hundreds of thousands of followers, plus SEO poisoning promoting trojanized software such as a Bluetooth driver and WinDirStat, infecting gamers and corporate endpoints including critical infrastructure and government entities. A single shared loader delivered payloads including Insomnia RAT, ARKTunnel, Docro Hijacker, GCleaner and Socks5Systemz between July 2025 and April 2026, with more than 10,000 distinct loader samples and over 200 rotating C2 domains identified. YouTube terminated the malicious channels after Unit 42 notified the platform.

Palo Alto Unit 42·updated Sep 11, 12:24 UTC · 4d ago·first Sep 9, 10:00 UTC · 6d agoMalware in the wild 2 sources1

[webapps] EasyAppointments 1.5.1 - Blind SQL Injection

A proof-of-concept exploit for a blind SQL injection vulnerability in EasyAppointments 1.5.1 has been published on Exploit-DB.

Exploit-DB lists a public proof-of-concept exploit for a blind SQL injection flaw in EasyAppointments 1.5.1. The listing falls under the webapps category and enables reproduction of the injection. No in-the-wild exploitation or CVE identifier is stated in the listing.

Exploit-DB·Sep 1, 00:00 UTC · 14d agoExploit / PoC1

Google Search Makes It Harder to See Where a Link Really Goes Before You Click

Google now routes some Search results through opaque google.com/goto redirects, weakening hover-to-verify anti-phishing checks.

Google has begun serving some organic search results as opaque google.com/goto?url= redirects whose destinations can only be resolved server-side by Google, likely to raise scraping costs for rank trackers and archival services. The change removes the pre-click hover preview of the true destination URL, undermining a long-standing anti-phishing habit for spotting lookalike, typosquatted, or search-optimized phishing domains. Security teams are advised to rely on layered defenses such as domain reputation, DNS and web filtering, browser isolation, and user training rather than hover text.

GBHackers·13:42 UTC · 9h agoIndustry

Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models

Empirical study finds direct whole-file generation beats iterative diff-based editing for Flutter/Dart code models on about 1,790 held-out tasks.

Researchers trained Rainbow-Pony-100M from scratch and fine-tuned Qwen2.5-Coder-0.5B in both direct-generation and diff-based regimes, then evaluated four resulting models on roughly 1,790 Flutter/Dart tasks. Direct generation outperformed diff-based generation on compilation pass rate, bits-per-byte, character-level similarity, and blinded LLM-judge ratings. Diff-based editing is competitive only on short, localized edits in refactoring and error-handling tasks, a property the authors call task locality.

Hugging Face daily papers·Sep 4, 20:00 UTC · 11d agoAI research1

ASCII smuggling isn't just an AI security risk

Microsoft tracked a phishing campaign peaking at 2.37 million daily messages that hid financial-lure keywords with invisible Unicode tag characters to evade filters.

Microsoft researchers uncovered a large phishing campaign that inserted invisible Unicode tag characters (e.g., U+E0020) inside common financial keywords like 'funding', splitting words so keyword, signature, and regex matches fail. The campaign peaked at more than 2.37 million messages in late February 2026, ran from about 150 finance-themed sender domains on a strict weekday-only schedule, and gradually declined to under 20% of peak weekday volume by late March, with residual spikes through mid-June. The technique repurposes ASCII smuggling, normally used for indirect prompt injection against AI assistants, for traditional email phishing evasion. Microsoft advises defenders to strip or fold invisible Unicode code points before content matching and to watch for bulk weekday spikes from churning finance-themed domains.

The Register · Security·Sep 4, 19:23 UTC · 11d agoPhishing & fraud in the wild1

Attackers conceal phishing lures using invisible Unicode characters

Threat actors use invisible Unicode characters (ASCII smuggling) to hide phishing lures and evade email security filters.

Threat actors have adopted the ASCII smuggling technique in phishing campaigns, embedding invisible Unicode characters in emails to conceal malicious lures. The approach is designed to evade email security filters that scan for visible phishing indicators. The report gives no victim counts or named campaigns.

BleepingComputer·Sep 6, 14:23 UTC · 9d agoPhishing & fraud in the wild