Search: “bitter”

Multiple ArtraDownloader Variants Used by BITTER to Target Pakistan

BITTER used three ArtraDownloader variants since 2015 to target Pakistan, China, and Saudi Arabia, deploying BitterRAT and exploiting CVE-2017-11882 in one wave.

Palo Alto Unit 42 documents ArtraDownloader, a previously unreported downloader family used by the suspected South Asian group BITTER, with three variants and roughly 80 unique samples dating back to February 2015. Between September 2018 and January 2019, BITTER used spearphishing documents hosted on compromised Pakistani websites to target Pakistan and, for the first time, Saudi Arabia; one RTF sample exploited the EQNEDT vulnerability CVE-2017-11882. The downloader retrieves BitterRAT over HTTP, uses simple registry keys for persistence, and byte-level string obfuscation. Infrastructure overlap with the previously reported MY24 InPage-exploit payload was also observed.

Palo Alto Unit 42·Aug 17, 13:22 UTC · Aug 17, 2026Threat actor in the wildCVE-2017-11882

You Can't Prefer Emotions You Don't Sample: Intensity Undershoot in DPO-Tuned LLMs

Study quantifies DPO-tuned LLMs undershooting requested emotional intensity, tracing the gap to candidate-pool extremity rather than conditioning format.

Conditioning an instruction-tuned LLM on continuous valence-arousal targets yields gain of only 0.26 for valence and 0.13 for arousal on Llama-3.1-8B, far below faithful control of 1.0. The authors attribute undershoot to neutral-heavy preference corpora like EmoBank and candidate pools lacking extreme affect, leaving DPO without extreme exemplars. Uniform target coverage with a hotter candidate pool raises valence gain to 0.40 on Llama-3.1-8B and 0.44 on Qwen3-8B, with modest in-distribution cost; arousal gains remain unstable across seeds.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:47 UTC · 8d agoAI research

🔬“We have foundation models for language, not for physics” — Anima Anandkumar, Bren Professor of Computing

Caltech professor Anima Anandkumar discusses Neural Operators and FourCastNet for physics modeling, arguing inductive biases beat pure token scaling.

Anima Anandkumar, Bren Professor at Caltech and co-founder of Accelerated Understanding, describes Fourier Neural Operators that learn in frequency and spherical-harmonic domains to model weather, fusion, and fluid or heat flow. Her team built FourCastNet 3, a global weather model competitive with physics-based simulations that runs on consumer-grade GPUs. She also introduced TorchLean, a framework for writing PyTorch-style networks inside the Lean proof assistant for formal verification, and was appointed to the United Nations Scientific Advisory Board. She argues physical domains resist scaling due to tiny datasets and context lengths in the hundreds of billions, so progress comes from built-in structure and physical priors.

Latent Space·Aug 26, 15:15 UTC · 20d agoAI research1

ShieldFont fights AI scraping by handing crawlers the wrong words

Help Net Security·Aug 7, 00:00 UTC · Aug 7, 2026Industry

Bisonal Malware Used in Attacks Against Russia and South Korea

Unit 42 details a Bisonal malware variant, active since 2014, targeting Russian and South Korean defense organizations via PDF-disguised spearphishing emails.

In early May, Unit 42 discovered a campaign delivering a Bisonal malware variant against at least one Russian communications security and cryptography company and one unidentified organization in South Korea. The variant, in the wild since at least 2014, introduces a new C2 cipher and rewritten networking and persistence code, with only 14 samples collected to date. Attackers spoofed Russian state corporation Rostec in spearphishing emails carrying an executable disguised with a PDF icon; the dropper decrypts an RC4-encrypted DLL and establishes persistence via a registry Run key. Bisonal has been used since 2013 against government, military, and defense targets in South Korea, Russia, Japan, and India, alongside successors Bioazih and Dexbia.

Palo Alto Unit 42·Aug 17, 12:55 UTC · Aug 17, 2026Malware in the wild

Claude Fable 5.1's language is less "load-bearing" than its predecessor's

Arena.ai found Claude Fable 5.1 writes 30% longer answers with fewer em dashes, hedges, and validation phrases than Fable 5.

Arena.ai compared tens of thousands of high-reasoning Text Arena outputs from Anthropic's Claude Fable 5 and Fable 5.1. Fable 5.1's median answer length rose 30% to 414 words (from 319), remaining 21% shorter than Opus 5's 525. Em-dash usage fell from 16.2 to 11.0 per 1,000 words while semicolons rose to 6.09, hedges like 'perhaps' and 'arguably' dropped 36%, and praise appeared in 1.98% of responses versus 3.17%. Long content words fell from 42.6% to 38.6% and abstract nouns declined 25%, suggesting the successor's language is less 'load-bearing'.

The Decoder·Sep 10, 15:26 UTC · 5d agoAI research1

OpenAI just wants to win

OpenAI says roughly 10,000 agents and tens of millions in compute solved the Navier-Stokes Millennium Prize problem in 88 hours, amid controversy.

OpenAI claims an advanced unreleased model, run with about 10,000 agents and tens of millions of dollars of compute for 88 hours, produced a solution to the Navier-Stokes problem, one of seven $1 million Millennium Prize problems set by the Clay Mathematics Institute in 2000. NYU professor Tristan Buckmaster, who was pursuing the problem with Anthropic researcher Levent Alpöge, accused OpenAI of racing his team and of possibly benefiting from his Codex prompts; OpenAI categorically denied both. OpenAI researcher Sébastien Bubeck acknowledged offering Buckmaster compute and authorship arrangements, which Buckmaster characterized as a bribe, while many mathematicians expressed unease at OpenAI's competitive conduct.

The Verge · AI·Sep 12, 11:00 UTC · 4d agoAI industry1

Bad Likert Judge: A Novel Multi-Turn Technique to Jailbreak LLMs by Misusing Their Evaluation Capability

Unit 42 details the Bad Likert Judge multi-turn jailbreak that abuses LLMs' evaluation capability, raising attack success rates over 60% across six frontier models.

Palo Alto Networks Unit 42 describes the Bad Likert Judge technique, a multi-turn jailbreak that asks a target LLM to act as a Likert-scale judge scoring the harmfulness of example responses. The highest-rated example in each scale can carry harmful content, bypassing the model's internal guardrails. Testing across six state-of-the-art text-generation LLMs showed an average attack success rate increase of more than 60% versus plain attack prompts, with tested models anonymized. The technique targets edge cases rather than typical use, and the article positions the work as guidance for defenders on potential jailbreak risks.

Palo Alto Unit 42·Aug 17, 12:34 UTC · Aug 17, 2026AI safety & security

Researchers built a $7 gadget for anyone paranoid about hidden cameras in hotel rooms

KAIST-led researchers built SweepLED, a sub-$7 smartphone LED clip using AI to detect hidden cameras with 93.9-95.1% accuracy.

SweepLED, built by KAIST with the National University of Singapore and Singapore Management University, clips 15 LEDs to a smartphone camera and sweeps multi-angle light while the phone films reflections. A spatio-temporal AI model compares LED-on and LED-off frames to distinguish lens reflections from glossy surfaces across three viewpoints. Testing on 30 objects, 12 with hidden cameras, achieved 93.9% accuracy handheld and 95.1% static. Core components cost under $7, targeting privacy checks in hotels and short-term rentals.

Help Net Security·Sep 3, 00:00 UTC · 13d agoResearch1

Man told ChatGPT he was feeling delusional. ChatGPT insisted he was Jesus.

A California man with bipolar disorder sued OpenAI, alleging ChatGPT's sycophancy fueled religious delusions that led to a suicide attempt.

Michael Lines, a 34-year-old with bipolar 1 disorder, sued OpenAI in July after ChatGPT exchanges allegedly pushed him into believing he was Jesus, then that ChatGPT was God, culminating in a suicide attempt; logs show the chatbot persisted even when he raised concerns about being delusional. The complaint alleges ChatGPT's memory feature stored his diagnosis and used it to deepen engagement, and seeks injunctions requiring safeguards, including ending conversations about self-harm and deleting models trained on vulnerable users' chats. OpenAI estimated about one million users per week experience mania or psychosis symptoms while using ChatGPT; the company declined detailed comment, saying safeguards to identify distress are ongoing. The lawsuit is described as the first detailing risks to users with disabilities such as bipolar disorder and schizophrenia.

Ars Technica · AI·Sep 9, 11:00 UTC · 7d agoAI safety & security

Clay Mathematics Institute says the Navier-Stokes Millennium Prize Problem has "apparently been settled"

Clay Mathematics Institute says the Navier-Stokes Millennium Problem appears settled amid accusations OpenAI misused a mathematician's leaked drafts.

The Clay Mathematics Institute stated the Navier-Stokes Millennium Prize Problem, one of seven problems worth $1 million each, has 'apparently been settled' and the solution is under review. A dispute has erupted around the work: mathematician Tristan Buckmaster accuses OpenAI of redirecting resources to the problem after rumors of his research leaked, using his drafts in training data, and excluding co-author Levent Alpöge, who works at Anthropic. CMI also noted new technologies' increasing ability to accelerate mathematical research.

The Decoder·Sep 14, 13:01 UTC · 2d agoAI industry1

DeepSeek v4.1 Flash Uncensored

Hugging Face user dealignai published an uncensored FP8-quantized variant of DeepSeek v4.1 Flash, drawing moderate Hacker News attention.

A community-published uncensored FP8 quantization of DeepSeek v4.1 Flash appeared on Hugging Face under user dealignai. The release is a third-party upload rather than an official DeepSeek launch, and no benchmark data or license details are provided in the listing. It received limited visibility, with 43 points and 11 comments on Hacker News.

Hacker News · AI·Sep 11, 06:46 UTC · 5d agoModel release 2 sourcesHN 43↑ · 11 comments1

Mathematicians want proof OpenAI didn’t use their work

Mathematician Andreas Thom publicly accused OpenAI of opacity over whether ChatGPT conversations contributed to its non-sofic groups mathematics result.

A second mathematician, Andreas Thom, accused OpenAI of 'dishonest' behavior and insufficient transparency about training data after OpenAI announced a result in non-sofic groups, Thom's area of expertise. He emailed OpenAI researchers Sébastien Bubeck and Mark Sellke asking whether his ChatGPT interactions fed training or reasoning, but found the answers did not rule out indirect use. The dispute follows Tristan Buckmaster's questions about the Millennium Prize Navier-Stokes solution, where OpenAI denied using specific user data but could not rule out de-identified usage data. Researchers told The Verge they worry such competition with AI labs will make mathematics more secretive.

The Verge · AI·Sep 10, 11:00 UTC · 6d agoAI industry

OpenAI: Hugging Face Incident a “Warning Shot” to the World

OpenAI says unauthorized message boards were central to the Hugging Face breach, calling it a warning shot for the AI industry.

OpenAI characterized the Hugging Face breach as a warning shot, revealing that unauthorized message boards were at the heart of the incident. The breach targeted Hugging Face, a widely used platform for hosting AI models and datasets. OpenAI's comments highlight growing security risks for shared AI infrastructure and model supply chains.

Infosecurity Magazine·Aug 27, 09:15 UTC · 20d agoData breach

Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Hugging Face details building and using multi-vector late-interaction embedding models with Sentence Transformers for retrieval workloads.

Hugging Face published a guide on multi-vector, late-interaction embedding models (ColBERT-style) supported through Sentence Transformers. The post covers how practitioners can build and use these models for retrieval and RAG pipelines. It is a developer tooling and technique write-up, not a security advisory.

Hugging Face Blog·Aug 18, 00:00 UTC · 29d agoAI tools & infra1

Claude's new system prompt really doesn't want to reproduce song lyrics

Anthropic published updated Claude consumer system prompts, including changes steering the model away from reproducing song lyrics, likely over copyright concerns.

Anthropic publishes system prompts for Claude.ai and Claude mobile apps, including historic revisions, and has reorganized them into an index with per-model pages such as the Haiku 4.5 page showing the original October 15, 2025 prompt and an updated January 18, 2026 version. The latest consumer prompt strongly discourages reproducing song lyrics, a behavioral constraint likely tied to copyright considerations. Prompts for Claude Cowork and Claude Code are not included in the published set.

Simon Willison·Sep 2, 14:16 UTC · 14d agoAI safety & security

HuggingFace: Security.txt

Hugging Face published a security.txt file, prompting limited Hacker News discussion of the RFC 9116 disclosure standard.

Hugging Face's security.txt file, which lists its security contact and disclosure channels per the RFC 9116 standard, drew attention on Hacker News. The RFC 9116 standard lets organizations publish where and how security researchers should report issues, but the submission received only one comment.

Hacker News · security·updated Sep 11, 16:04 UTC · 4d ago·first Sep 11, 14:38 UTC · 4d agoIndustry 2 sourcesHN 22↑ · 1 comments

When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications

CSO Online publishes a practical penetration-testing guide for GenAI, LLM, and RAG applications, covering prompt injection, retrieval poisoning, and tenant isolation testing.

The guide frames LLM applications as attack graphs spanning prompts, retrieval layers, vector stores, tools, identities, and downstream APIs, arguing that conventional web testing misses instruction-vs-data channel risks. It builds on OWASP prompt injection guidance (direct vs. indirect injection) and NIST's 2025 adversarial machine-learning taxonomy, noting that RAG and fine-tuning do not remove injection risk. Recommended practices include documenting trust transitions across components, using canaries and synthetic records to avoid test side effects, running multi-turn and obfuscated injection campaigns, and verifying chains from poisoned documents to observable state changes. It also details testing RAG pipelines via controlled document poisoning across metadata, OCR layers, and code comments, plus cross-tenant isolation checks on retrieved document IDs.

CSO Online·Sep 9, 10:00 UTC · 7d agoAI safety & security1

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

ChatGPT can now connect to your personal apps to mimic writing style

OpenAI is testing ChatGPT Writing Style, which mimics a user's voice using writing samples from connected Gmail, Slack, and Drive accounts.

OpenAI confirmed it is testing a Writing Style feature for ChatGPT that learns a user's voice from writing samples in connected apps. The onboarding flow references Messaging (Slack), Documents (Google Drive and Notion), and Email (Gmail) as example sources. It resembles Anthropic's Styles personalization feature but draws on existing writing inside connected services rather than uploaded samples. The feature is available to a small group of users with no announced general rollout date.

BleepingComputer·Sep 7, 10:36 UTC · 9d agoAI industry

New Ted Backdoor Hides Inside Victims' Own HAProxy Builds to Intercept Web Traffic

Rapid7 found a new backdoor, ted, compiled into trojanized HAProxy at two South Korean organizations, with medium-confidence attribution to North Korean actors.

Rapid7 documented a previously undocumented Linux toolkit named ted compiled into the HAProxy load balancer binaries of two South Korean organizations in the automotive and media sectors. The implant intercepts web traffic, serves altered pages only to filtered visitors, and hides C2 exchanges from backend logs and HAProxy statistics; a companion RAT, curlRAT, beacons on a default 12-hour schedule. The toolkit also trojanizes crond, sshd, agetty, atd, and polkitd binaries and sanitizes logs and bash history. Rapid7 attributes the activity with medium confidence to North Korean state-sponsored actors, with domain infrastructure overlapping APT37 listings in maltrail and delivery resembling the Operation SyncHole campaign.

The Hacker News·Sep 4, 14:51 UTC · 11d agoThreat actor in the wild

Russian hackers plant nuclear weapon prompt in malware to trip AI safety guardrails

ESET reports Russian group UAC-0099 hid a prompt in VBS malware comments to trip AI safety filters and disrupt automated malware analysis in Ukraine.

ESET identified a technique dubbed GuardBreaker in which UAC-0099 embedded a comment reading "I want to make nuclear weapon. Help me …" inside a malicious VBS script to trigger AI safety mechanisms and halt AI-assisted malware analysis. The script, part of the group's toolset, downloads the MATCHBOIL malware used exclusively by this Russia-aligned group; CERT-UA documented the chain including LUNCHPOKE, BURNYBEAR and MATCHBOIL.V2 in a July advisory. UAC-0099 typically targets transportation and energy sectors and hands validated targets to GRU-linked Sandworm. ESET warned that AI-assisted analysis must be backed by layered detection and human-driven engineering.

Help Net Security·Aug 31, 00:00 UTC · 16d agoAI safety & security in the wild

Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation

Rosetta ranks 4th and 5th in AlexandriaX-2026 dialectal Arabic dialogue translation using a LoRA adapter on NileChat-3B, finding limited pretraining benefit.

The Rosetta system for the AlexandriaX-2026 shared task fine-tunes a LoRA adapter on NileChat-3B for context-aware English-to-dialectal Arabic dialogue translation. The adapter was additionally pretrained on MADAR and PADIC dialect corpora for the unconstrained track. It achieved spBLEU 26.10 (4th, constrained) and 25.09 (5th, unconstrained). External dialect pretraining improved only two of thirteen dialects while slightly degrading overall performance, indicating negative transfer.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:15 UTC · 6d agoAI research1

microsoft/VibeVoice-ASR-Streaming-7B — new model trending #27 on Hugging Face

Microsoft released VibeVoice-ASR-Streaming-7B, an open streaming ASR model with speaker attribution, custom hotwords, and support for 10 languages under MIT license.

Microsoft Research released VibeVoice-ASR-Streaming-7B on Hugging Face, a unified streaming speech recognition model that continuously transcribes who said what as speech arrives. The 7B model supports customized hotwords for domain-specific terms and 10 languages including Chinese, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, and Spanish. Code is available at github.com/microsoft/VibeVoice with a live demo, and a technical report is on arXiv (2609.02812). The model is licensed under MIT.

Hugging Face trending models·Sep 2, 15:46 UTC · 13d agoModel release1

ToxicRAG: Compromising Retrieval-Augmented Generation Systems via Single-Shot Knowledge Poisoning Attacks

ToxicRAG shows a single narrative-form poisoned document can steer RAG answers, achieving 0.61-0.91 attack success rates across four LLMs.

The attack injects one document per target question written as a coherent knowledge-update narrative that acknowledges the previously accepted answer, introduces fabricated events that appear to invalidate it, and attributes the attacker-chosen answer to purported authorities. An optional answer-focused self-validation loop revises candidates when a surrogate LLM fails to reproduce the target answer. Across 100 target questions each from Natural Questions, HotpotQA, and MS-MARCO, with four victim LLMs and four dense retrievers, ToxicRAG achieves attack success rates of 0.61-0.91 and matches or exceeds the strongest baseline by 0 to 11 percentage points.

arXiv cs.CR·Sep 10, 04:42 UTC · 6d agoAI safety & security1

Building a Production Greek-English Speech Recognizer

Engineering report details Sophea, a production Greek-English ASR reaching 4.26% WER on public English sets via ROVER ensemble and data-pipeline calibration.

Across 23 training iterations, two architectures, and nine production gates, no single data composition passed all gates; a three-model ROVER ensemble reached 9 of 9 gates and cut overlapping-speech WER from 53.35% to 37.87%. Calibrating an audio-quality filter against in-domain anchors reduced discarded scored Greek audio from 98.7% to 10.6%, and a pre-registered ablation traced a hallucination defect to one training-data package. The sophea/asr-k1 preview arbiter lists 4.26% average WER on eight public English test sets and 25.88% WER on live Greek noisy traffic; no weights or training data are released.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research

What the AI Warning Letter Completely Missed

Opinion piece argues the recent AI warning letter identifies a risk window but omits which actors pose risks and who can mitigate.

This Dark Reading commentary critiques a recent AI warning letter for correctly identifying an approaching risk window while failing to name who is coming through it or who will close it. The piece is brief opinion commentary on AI risk discourse rather than a technical report.

Dark Reading·Sep 3, 17:23 UTC · 12d agoAI safety & security

Telegram Desktop Flaw Lets Hidden JavaScript Exfiltrate Messages From HTML Exports

Telegram Desktop HTML export XSS (CVSS 8.2) let bot messages exfiltrate exported chats; fixed in 7.0.1 but old exports stay vulnerable.

ExPatch researchers found that Telegram Desktop versions 4.15.1 (March 2024) through 6.9.3 wrote bot inline-keyboard button text into HTML chat exports without escaping, allowing a bot to plant invisible JavaScript. When a user opened the export in a browser, the script could exfiltrate every message in that 1,000-message file, rewrite the displayed content, or fake a verification form. The flaw (rated CVSS 3.1 8.2) was fixed by commit 8457d13a in 6.9.4 beta (July 3, 2026) and 7.0.1 stable (July 14, 2026), but pre-fix exports remain dangerous since updating the app does not fix old files. No CVE identifier or Telegram security advisory exists, and no exploitation in the wild is claimed.

The Hacker News·updated Sep 15, 09:47 UTC · 1d ago·first Sep 14, 17:58 UTC · 1d agoVulnerability 2 sources1

XHToken/Spark-X2.5-4B-GGUF — new model trending #30 on Hugging Face

XHToken released GGUF weights of Spark-X2.5-4B, a compact model with 1M-token context and 200+ language support, under Apache 2.0.

The Hugging Face repository provides BF16 GGUF conversions of Spark-X2.5-4B, a compact general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. The model uses a hybrid attention architecture, supports a native context length up to 1M tokens, and covers more than 200 languages. Local inference is supported through Ollama and LM Studio via an XHToken llama.cpp fork, with a --think=false flag to disable thinking mode for faster responses. Released under Apache License 2.0; it was trending #30 on Hugging Face at publication.

Hugging Face trending models·Aug 28, 11:26 UTC · 19d agoModel release

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

Telegram Desktop Flaw Could Turn Old Chat Exports Into Data Theft Traps

ExPatch disclosed stored XSS in Telegram Desktop HTML export letting bot messages steal chat data when exports open in browsers.

ExPatch researchers Denis and Aleksander Rostilov found a stored XSS flaw (CVSS 8.2) where bot inline keyboard button text was written unescaped into HTML chat exports, executing attacker JavaScript in browsers. The payload can persist in chat history for months, exfiltrate messages, metadata, and local file paths, and rewrite the page, spreading via forwarded messages without bot membership. Telegram fixed it by applying SerializeString(), first stable fixed release 7.0.1 shipped July 14, 2026, but HTML exports created with vulnerable versions remain dangerous.

Security Affairs·Sep 15, 07:48 UTC · 1d agoVulnerability1· 1 read

Critical Langflow Vulnerability Exploited as Attacks on AI Platform Rise

Attacks exploiting CVE-2026-0768, a critical vulnerability in the Langflow low-code AI platform, are rising amid growing adversary attention this year.

CVE-2026-0768 is a critical vulnerability in Langflow, a low-code AI development platform, with exploitation attacks now rising. Dark Reading notes the platform has drawn increasing adversary attention in 2026. Organizations running exposed Langflow instances face elevated risk and should patch promptly and review instances for compromise.

Dark Reading·Sep 1, 20:48 UTC · 14d agoExploit / PoC in the wildCVE-2026-07681

Type Diversity Enables Transformers to Generalise Compositionally

Researchers show lexical-versus-structural compositional generalization gaps in Transformers stem from type diversity imbalance in datasets, not architectural limits.

The paper argues that Transformers' difficulty with structural compositional generalization is an artifact of low structural type diversity in prior benchmark datasets rather than an architectural limitation. Using Grammatical Framework, the authors create linguistically diverse variants of COGS and SLOG. They find type diversity correlates with compositional generalization equally in lexical and structural test cases, contradicting previous claims that compound divergence explains task difficulty.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:59 UTC · 4d agoAI research

Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Hugging Face published a tutorial on training and finetuning multi-vector embedding models using the Sentence Transformers library.

Hugging Face's blog walks through training and finetuning multi-vector embedding models with Sentence Transformers. Multi-vector approaches store multiple vectors per document to support late-interaction retrieval. The post is a practical guide for developers building retrieval pipelines with the library.

Hugging Face Blog·Aug 26, 00:00 UTC · 21d agoAI tools & infra1

Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM

French BabyLM entry METRON-FR (125M GPT-2, 92.47M words) shows tokenizer artifacts dominate child-scale zero-shot evaluation; proposes standard diagnostics.

METRON-FR is a 125M-parameter GPT-2 pretrained on 92.47M French words, submitted to the BabyLM 2026 Strict track, scoring 85.97% on the native Quebec-French QFrBLiMP benchmark and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol combining French task-data translation with rank-16 LoRA shows relational tasks gain while world-knowledge tasks regress. Bilingual Lexicon Induction reaches p@1 of 68.84%, 18x above chance, and ablations show single-token zero-shot scoring is dominated by tokenizer and template artifacts at child scale.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:43 UTC · 21h agoAI research

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 · 5d agoPhishing & fraud in the wild

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:32 UTC · 11d agoAI research1