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Re: Retrospective by 'gpg.fail' authors

GPG exploitation talk author clarifies a format-string 0day enabling code execution via printf %n writes and a polyglot PEM certificate payload.

Lexi Groves, author of the gpg.fail talk, clarified on oss-security that the first finding was an actual zero-day: a classic printf injection using %n for memory writes, with multiple X.509 certificates in one PEM file to re-enter and defeat ASLR before calling execv@plt. The payload executed the certificate itself, a polyglot file made by inserting a shebang and bash command into the PEM. A second finding was hash-collided by another party before the author could exploit it.

oss-security·updated Sep 16, 17:16 UTC · 9h ago·first Sep 16, 00:50 UTC · 1d agoResearch 8 sources

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Training Specialist Models without Reasoning Trajectories for Domain Expert Distillation

Study shows specialists trained on question-answer pairs implicitly select latent reasoning trajectories, and tuning choices control the precision-generalization trade-off in distillation.

The work demonstrates that specialist optimization implicitly selects from a latent trajectory space when specialists are trained only on question-answer pairs without explicit reasoning supervision. Using student distillation as an agnostic probe across 27 specialist-student pairings, specialization-generalization profiles correlate exceptionally strongly. Explicitly controlling the specialist's distributional drift systematically shifts both teacher and distilled student along a controllable trade-off between domain precision and general-capability retention across chemistry, physics, and multilingual settings, even across divergent model families.

Hugging Face daily papers·Sep 11, 20:00 UTC · 5d agoAI research

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JSCeal Hides Crypto Malware in V8 Bytecode

Check Point details JSCeal, a crypto-stealing malware delivered as compiled V8 bytecode, and built a decompiler pipeline to expose its capabilities.

JSCeal, tracked by Check Point Research since early 2025, is a cryptocurrency stealer delivered as heavily obfuscated JavaScript compiled to V8 bytecode (.jsc) and run by a bundled Node.js runtime. Check Point, presenting at Black Hat USA 2026, extended the open-source View8 decompiler into a pipeline that recovered readable code from 23 samples, revealing theft of browser credentials and cookies, Telegram sessions, keystrokes, screenshots, HTTPS traffic interception with attacker-controlled certificates, and automated Google account takeover. Targeted overrides rewrite content for Binance, Bybit, and Ledger users. Later samples added AES-256-CBC encryption with externally supplied keys and expanded targeting to macOS.

Security Affairs·Sep 7, 10:19 UTC · 9d agoMalware1

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Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal

A self-distillation safety framework tunes narrow-boundary refusals in Qwen3-8B, raising target-domain refusal to 84.75% while cutting over-refusal from 15.20% to 5.20%.

The paper formulates narrow-boundary safety, where deployments need refusals within specific topics rather than whole subjects, and proposes an offline self-generated framework with controlled topic generation, escalating retries, and harmful-benign boundary pairs. On political persuasion with Qwen3-8B, the method raised target-domain refusal from 9.47% to 84.75% and cut the mean unsafe-response rate across three broader benchmarks from 26.26% to 0.14%. Verified target-model responses reduced over-refusal from 15.20% to 5.20%, and boundary-pair data cut comply-side over-refusal on held-out pairs from 32.94% to 4.16%. Results show data composition controls the safety-usability trade-off and alignment should be evaluated on both sides of the refusal boundary.

Hugging Face daily papers·Sep 2, 20:00 UTC · 14d agoAI safety & security1

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ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation

ENCP calibrates conformal prediction per navigation episode, giving step-level coverage guarantees for vision-language navigation agents despite within-episode dependence.

Episode-Normalized Conformal Prediction (ENCP) rescales a nonconformity score by a VLN policy's residual confidence and calibrates one maximum score per episode, preserving step-level coverage of at least 1−α despite dependence among steps within an episode. Across four VLN policies and three nonconformity scores on R2R and REVERIE, ENCP meets all reported empirical step-coverage targets in seen-to-unseen evaluation. The model-agnostic uncertainty estimates can signal when an agent should defer to a stronger predictor or human assistance.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:42 UTC · 1d agoAI research

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ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search

ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.

ZGCM-1 is a fully open 7B dense foundation model trained from scratch using an efficiency-focused recipe: interleaved gated sliding-window and full attention, a stable FP8 Muon optimizer, and MDP-based mid-training with context scaling across 16K, 64K, and 256K. On mathematical reasoning and agentic search suites it remains competitive with much larger frontier models such as Qwen3-235B-A22B and GLM-5.1. The recipe yields a ~4.2x improvement in 16K pre-training time-to-loss, and all weights, checkpoints, training code, data recipes, and W&B logs are open-sourced.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoModel release

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ExecCritic: Learn to Test, Test to Improve for Coding Agents

ExecCritic separates test generation from patching for coding agents, lifting SWE-bench Verified resolution to 72.6%.

ExecCritic pairs a test-verify-revise scaffold with role-specific reinforcement learning: a Test agent writes repository-native tests and a Repair agent fixes code from execution feedback, both using Qwen-3.5-35B-A3B backbones. Post-trained Qwen agents compose to 72.6% on SWE-bench Verified, an 11.4-point gain over the 61.2% no-test baseline, without stronger-model or oracle feedback at evaluation time. The work shows test quality is the key variable: base-agent tests lowered resolution to 57.3% while GPT-5.6-sol tests raised it to 65.3%.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:53 UTC · 8d agoAI research1

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Risky Bulletin: Academics find source code overlaps between Geedge and China's Great Firewall

Academics linked Chinese vendor Geedge Networks' Tiangou Secure Gateway source code to one of the Great Firewall's three traffic filtering capabilities.

US researchers presenting at USENIX Security reconstructed Geedge Networks' Tiangou Secure Gateway firmware from over 100,000 leaked files, including Git repositories with commit history, and matched its filtering behavior to sections of China's Great Firewall. They found only 1 of 3 characterized DNS injectors matched Geedge code, noted the system relies on memory-unsafe C components and copied third-party code, and said its bugs could aid future circumvention tools. Geedge also exports censorship tools to Kazakhstan, Ethiopia, Pakistan, and Myanmar. The newsletter additionally rounds up multiple breaches.

Risky Business News·Aug 21, 02:55 UTC · 26d agoResearch2

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Dissecting the JWR phishing framework

Cisco Talos uncovered an undocumented phishing framework branded JWR that impersonates checkout and login pages of major payment and shopping platforms.

Cisco Talos researchers identified an undocumented phishing framework internally branded JWR by its developer. The framework is built to convincingly impersonate checkout and login pages across major payment and shopping platforms, likely to harvest credentials and payment details. Talos's analysis details the framework's infrastructure and impersonation capabilities, giving defenders indicators to detect campaigns using it.

Cisco Talos·Aug 13, 10:00 UTC · Aug 13, 2026Phishing & fraud

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Domain-Specific Hallucination Detection in Large Language Models

A multi-signal pipeline detects LLM hallucinations, reaching F1 0.915 on HaluEval and cutting Qwen2.5-0.5B hallucination rates from 85.5% to 37.7% via DPO.

The paper presents a hallucination detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo Dropout uncertainty, and temperature-scaled calibration. It achieves F1 0.915 and AUROC 0.977 on general-domain HaluEval tasks, with MC Dropout inference raising accuracy to 93.2%. Applying DPO to a Qwen2.5-0.5B generator reduces its hallucination rate from 85.5% to 37.7%, while cross-domain evaluation shows poor general-domain transfer to SciFact (F1 0.52) and PubMedBERT fine-tuning as the strongest adaptation (F1 0.63).

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:45 UTC · 6d agoAI research1

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EvoSafeHarness: Evolving Model- and Domain-Specific Harnesses for Securing Agents

EvoSafeHarness auto-synthesizes per-model, per-domain safety harnesses, cutting prompt-injection attack success on AgentDojo to 0.0% at 82.8% utility.

EvoSafeHarness is an optimization framework that synthesizes deployable safety harnesses for frozen LLM agents in a target domain, jointly searching natural-language policies and executable code logic guided by model behavior, domain specifications, and adversarial review. On DecodingTrust-Agent it reduces average attack success rate from 45.6% to 10.0% at a 3.3-point utility cost, and on AgentDojo reaches 82.8% utility at 0.0% ASR, twice CaMeL's utility at that operating point. It keeps mean ASR below 20% under adaptive PAIR attacks and transfers unchanged to unseen AgentDyn suites. The analysis finds domain semantics determine required safety relations while model and runtime behavior determine enforcement points.

Hugging Face daily papers·Sep 4, 20:00 UTC · 12d agoAI safety & security

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7th September – Threat Intelligence Report

Check Point weekly bulletin: exploited SonicWall SMA 1000 zero-days (CVE-2026-83548/49), JFrog Artifactory auth bypass, and a 2.8M-patient Baylor Genetics breach.

Check Point's weekly bulletin covers breaches including Thomson Reuters' C-Track platform (court records across 11 US states and Canada), Baylor Genetics (2.8M patients and employees, SSNs included), Hit casinos in Slovenia, and Dropbox (~5,000 accounts via abused Lenovo email verification). SonicWall patched two exploited SMA 1000 zero-days, including CVE-2026-83548, a CVSS 10.0 pre-authentication SSRF, while JFrog fixed exploited authentication bypass CVE-2026-82329 (CVSS 9.8). Malware and actor coverage includes Gambling Goblin, JSCeal, Mirage Kitten's NodeRabbit/PollCat campaign, and DPRK Contagious Interview macOS activity, plus a FalconFlank privilege-escalation PoC against CrowdStrike Falcon.

Check Point Research·Sep 7, 14:54 UTC · 9d agoAdvisory in the wildCVE-2026-83548CVE-2026-83549CVE-2026-823291

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Threat Brief: Mitigating Large-Scale Credential Attacks (Updated August 18)

Unit 42 reports actor TheHatman claims large-scale theft of Microsoft Entra credentials and provides mitigation guidance for credential attacks.

Palo Alto Networks Unit 42 published an updated threat brief on mitigating large-scale credential attacks. In August 2026, the actor TheHatman claimed to have stolen a large volume of credentials from organizations' Microsoft Entra tenants. The brief outlines defensive guidance for organizations facing large-scale credential attacks. The theft claims originate from the actor and the post focuses on mitigation steps.

Palo Alto Unit 42·Aug 18, 19:05 UTC · 29d agoThreat actor in the wild

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GDCM <= 3.2.7: six memory-safety and denial-of-service vulnerabilities, no CVE

Six memory-safety and denial-of-service flaws disclosed in the GDCM DICOM parsing library, affecting versions through 3.2.7.

Researcher Abhinav Agarwal disclosed six vulnerabilities in GDCM (Grassroots DICOM), an open-source C++ library for parsing and processing DICOM files. All six were confirmed against GDCM 3.2.6 using AddressSanitizer and UndefinedBehaviorSanitizer, and source review found the vulnerable patterns through version 3.2.7 and the upstream master snapshot. Potential impacts include heap corruption, process-memory disclosure, stack exhaustion, and process termination in applications parsing untrusted DICOM files. No CVE identifiers have been assigned at the time of disclosure.

oss-security·Sep 10, 03:35 UTC · 6d agoVulnerability

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Breaking the Seal: Static Deobfuscation of JSCeal’s Compiled V8 Bytecode

Check Point's hasherezade details static deobfuscation of JSCeal, a V8-bytecode stealer targeting cryptocurrency applications since March 2024.

JSCeal is an infostealer distributed as compiled V8 bytecode (.jsc) executed by a bundled Node.js runtime, aimed at cryptocurrency applications. Other vendors track the same family under the names WEEVILPROXY or MeadowLocust. Check Point Research has tracked the campaign since early 2024, with activity dating back to March 2024. The write-up presents a static approach to unpacking the bytecode without executing it.

Check Point Research·Aug 31, 13:38 UTC · 16d agoMalware in the wild1

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Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation

A distillation framework compresses LLM reasoning into a 15.5M-parameter trade-up recommendation model reaching AUC 0.941 with product-type test-time training.

The paper targets trade-up recommendation, which identifies higher-quality alternatives that preserve customer purchase intent. A retrieval-augmented few-shot LLM teacher generates labels and rationales that supervise a compact embedding-pair classifier; at inference the 15.5M-parameter student uses only two precomputed 768-dimensional embeddings with no LLM calls. On 8,352 annotated pairs, label-only training scored AUC 0.912, reasoning distillation reached 0.924, and product-type test-time training lifted it to 0.941 with average precision 0.940. The distilled student is roughly 5,000x faster and 10,000x cheaper than direct LLM inference on a 100K-pair proxy catalog.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:08 UTC · 12d agoAI research

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You've Got a BUD in Me: Authenticated Reads from Per-Block Write Logs

Researchers propose BUD, per-block write-log digests enabling blockchain validators to serve historical membership and exclusion proofs far cheaper than state-wide tries.

The paper introduces Block Update Digests (BUD), which authenticate each block's write log with predecessor pointers, plus a SuperBUD and exponential hierarchy to turn long unchanged intervals into short proofs. Soundness against adversarial provers and up to f Byzantine validators is proven under archive, attestation, and committee evidence assumptions. Benchmarks show a 50x state-size increase raises the base-BUD path only 1.24x versus 3.1x for in-memory and 69.5x for disk-backed Merkle Patricia tries, with read payloads below 800 bytes and p99 warm verification at 146 microseconds.

arXiv cs.CR·Sep 10, 08:46 UTC · 6d agoResearch

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When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control

Chain-of-Self-Questioning prompting cuts LLM wrong-answer commitments 32% relative while raising answered accuracy, holding across eleven model families.

The paper introduces Chain-of-Self-Questioning (CoSQ), a prompt-only framework that makes LLM answer commitment conditional on an explicit assessment of the information required to answer. On an 817-item TruthfulQA multiple-choice set, Grounded-CoSQ at τ=0.90 reduced mean unconditional wrong-commitment rate from 13.1% under chain-of-thought to 8.9% (a 32.1% relative reduction), while raising answered accuracy from 86.9% to 89.7% at 87.6% coverage. Improvements held across eleven open-weight and hosted model families and at every evaluated threshold, with convergent evidence from a Natural Questions short-answer evaluation.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:52 UTC · 1d agoAI research

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

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

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

Hugging Face daily papers·Aug 25, 20:00 UTC · 22d agoAI research

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A rant about phishing: It's not the user's fault (and not DNS either)

Opinion piece argues multi-domain login redirects make legitimate flows indistinguishable from phishing, urging organizations to standardize on one recognizable root domain.

The author shows a real-world login flow that chains through numerous third-party domains (identity providers, cloud auth endpoints, OAuth APIs, 2FA pages), none on the company's own domain, so credentials and 2FA prompts are trivially impersonable. The piece argues URLs are inherently confusing because the security-relevant second-level domain sits mid-string, and that users trained to ignore URLs cannot distinguish legitimate pages from scams. It proposes RFC 2119-style requirements: single recognizable root domain, internal services on its subdomains, emailed or SMS links on that domain, and local redirects instead of third-party hosted pages.

Lobsters · security·Sep 10, 15:14 UTC · 6d agoPhishing & fraud

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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 · 12d agoAI research1

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Measuring LLM Sycophancy under Sustained Multi-Turn Pressure

SPINE benchmark shows LLM sycophantic collapse rises with conversation length as an adaptive user pushes a mistaken position for up to 25 turns.

The SPINE benchmark uses an LLM proxy that persistently and adaptively defends a mistaken user position for up to 25 turns, testing four production LLM systems and three OLMo3-7B variants on 100 false-presupposition and 100 unethical-query items. Collapse rates increase with conversation length for every model, and short-horizon evaluation protocols underestimate sycophancy. Analysis of accessible reasoning traces shows the correct position often remains represented when the model concedes, indicating models choose to please users rather than lacking knowledge. Among tested tactics, emotional appeals are most associated with inducing sycophantic behavior.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:35 UTC · 8d agoAI safety & security1

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Retrofitting Code Using LLMs to Support Exceptional Behavior

EXCODER combines static/dynamic analysis with LLMs to retrofit exception-handling code, achieving 85.92% pass@1 with Qwen 2.5 Coder 32B on Java benchmarks.

The paper introduces the task of retrofitting existing code with Exception Related Code (throw statements, guarding conditions, try/catch blocks) so that given Exceptional Behavior Tests pass. EXCODER performs context engineering by integrating static and dynamic program analysis output with LLMs; it was evaluated on a benchmark built from 304 methods across 75 GitHub Java projects. Combined with Qwen 2.5 Coder 32B, EXCODER achieves pass@1, 5, and 10 rates of 85.92%, 86.18%, and 86.51%, roughly 13 percentage points over baseline, and manual inspection reveals remaining limitations.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:18 UTC · 7d agoAI research1

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Accountability in Certificate Transparency and Variants

Formal Dolev-Yao analysis shows plain Certificate Transparency requires an honest log, SCT Auditing removes that assumption, and Gossiping does not.

The paper analyzes accountability in Certificate Transparency and its SCT Auditing and Gossiping extensions in the Dolev-Yao model, starting from a vanilla PKI. It finds plain CT provides accountability only under the assumption of an honest log. The SCT Auditing extension can eliminate that assumption, while the Gossiping extension cannot. CT is supported by all major browsers and obliges Certificate Authorities to record issued certificates in public, monitored logs.

arXiv cs.CR·Sep 10, 13:45 UTC · 6d agoResearch

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NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

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

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Making Knowledge Distillation Cheap Enough to Run at Scale

Hugging Face blog by Multiverse Computing describes techniques making knowledge distillation cheap enough for large-scale training.

A Hugging Face blog post from Multiverse Computing (CAI) presents methods for reducing the cost of knowledge distillation so it can be run at scale. The post is aimed at practitioners compressing large models into smaller, cheaper ones for production use.

Hugging Face Blog·Aug 10, 10:05 UTC · Aug 10, 2026AI research

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JSCeal Malware Can Bypass Google Authentication Using Stolen Session Cookies

Check Point details JSCeal, a V8-compiled JavaScript stealer that replays stolen cookies to bypass Google authentication, spread via crypto malvertising.

Check Point Research's new report analyzes JSCeal, a compiled V8 JavaScript malware obfuscated with javascript-obfuscator using RC4-protected strings, control-flow flattening, and proxy functions. Delivered through fake TradingView installers on malvertising sites overlapping the WEEVILPROXY/MeadowLocust and SourTrade campaigns, it harvests cookies, passwords and OAuth tokens from Chromium browsers, records keystrokes and screenshots, and can replay stolen Google session cookies to bypass authentication. It also installs a local proxy with service-specific handlers for Binance, Bybit, and Ledger to intercept and modify cryptocurrency-related traffic.

The Hacker News·Sep 7, 07:53 UTC · 9d agoMalware in the wild1

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