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CategoryAllRansomware 70Malware 58Vulnerability 52Data breach 33Exploit / PoC 27Threat actor 24Phishing & fraud 23Policy & legal 16Industry 15Research 13AI safety & security 6AI research 5AI industry 5Model release 2
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The AI ‘Ghosts’ Contaminating Academic Publishing

Samsung and University of Warsaw researchers find LLMs repeatedly generate the same fake author names, contaminating academic records with 1,655 ghost-authored DOIs.

A preprint from Samsung and the University of Warsaw, "The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing," shows that LLMs such as Claude, ChatGPT, and Gemini repeatedly generate the same fictional names like Elena Vasquez, Marcus Chen, and Aris Thorne as experts and co-authors. Researchers identified 1,655 ghost-authored records on CERN-operated Zenodo carrying real DataCite DOIs, fabricated journals, and backdated publication dates. Ghost names also form synthetic research groups on ResearchGate and are indexed without verification by Google Scholar and Semantic Scholar. The researchers suggest correlated name priors could serve as provenance signals for detecting AI-generated content.

404 Media · 20d agoAI research

Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

METR analysis finds AI accelerating cyber vulnerability discovery, while SPADE self-play environment generation improves Qwen3 reasoning benchmark scores at 30B scale.

Import AI 470 discusses a METR research note reporting differential acceleration from AI: major acceleration in reported cyber vulnerabilities (cURL, OpenSSL, Firefox, Microsoft, NVD, OSV), minor acceleration in mathematics, and no measurable acceleration in AI-research optimization benchmarks. It also covers SPADE, a self-play framework from a multi-university team (University of Washington, Stanford, MIT, CMU, and others) that co-evolves executable training environments and agent capability using Environment Designer and Reasoning Agent roles with hint-based regret rewards. Trained on Qwen3-4B-Instruct-2507, Qwen3-8B, and Qwen3-30B-A3B-Instruct-2507 via GRPO (400 rollouts of 25 environments), SPADE lifted the 30B-A3B game-environment suite average to 58.3, +8.1 over base, and improved tool-use results across backbones. The issue also references Hawkeye for building better GPU kernels.

Import AI · 23d agoAI research1

Trump may be forced to reveal secret rules feds use for AI safety testing

Protect Democracy sued four federal agencies to force disclosure of the administration's secret framework for frontier AI safety reviews.

Nonprofit Protect Democracy sued four federal agencies, including the Office of the National Cyber Director, OSTP, Treasury and Commerce, seeking disclosure of the secret voluntary framework used for pre-release safety reviews of frontier AI models. The complaint demands the framework text, participant identities and selection criteria by September 30, alleging OpenAI negotiated a private agreement limiting distribution of its cutting-edge models to government-vetted partners. The suit follows the launch of the GOLD EAGLE clearinghouse and the completion of the review framework on August 3, with California Senator Josh Becker supporting the request while the state considers the SB 813 bill for transparent AI safety standards.

Ars Technica · AI · 14d agoAI policy

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark with 660 instances measures whether LLMs can spot and fix underspecified research-method details for faithful implementation.

Researchers introduce IdeaAMBIG, a benchmark of 660 evidence-grounded instances (163 real-world gaps from reproducibility reports and GitHub issues, 497 controlled synthetic gaps) built from papers, codebases, and reproduction artifacts. It evaluates codification-readiness assessment, defect localization, and clarification action generation. Across 13 LLMs, the best model achieved only 9.6% Macro Defect Recovery Rate on real-world instances but 80.6% clarification success when given the annotated defect. An oracle study showed gold resolutions raise the codification-ready rate from 14% to 98%, identifying defect localization as the main bottleneck.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research2

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark of 660 specification-gap instances shows LLMs localize implementation-critical research gaps poorly, with best model at 9.6% defect recovery.

IdeaAMBIG is a benchmark of 660 evidence-grounded instances evaluating whether research-method specifications provide enough information for faithful implementation: 163 real-world gaps from reproducibility reports and GitHub issues plus 497 controlled synthetic gaps. It tests codification-readiness assessment, defect localization, and clarification action generation across 13 LLMs. The best model achieves only a 9.6% Macro Defect Recovery Rate on real-world instances, though 80.6% clarification success when given the annotated defect, and an oracle study shows gold resolutions raise codification-ready rates from 14% to 98%. Defect localization emerges as the main bottleneck across all evaluated models.

Hugging Face daily papers · 8d agoAI research

Thelio Mira AI Linux Workstation: 192 GB GPU Memory

System76 launches the Thelio Mira AI Linux workstation from $3,299 with dual NVIDIA RTX Pro 6000 GPUs and 192 GB GPU memory for local AI workloads.

System76's Thelio Mira AI is a locally built (Denver, Colorado) Linux workstation for AI training, fine-tuning, and inference, starting at $3,299. Configurations go up to a 16-core AMD Ryzen 9000 CPU, 192 GB DDR5 RAM, and dual NVIDIA RTX Pro 6000 Blackwell GPUs delivering 192 GB of (ECC) GPU memory with liquid cooling, dual PCIe 5.0 x16 slots, and up to three M.2 NVMe drives. It ships with Pop!_OS 24.04 LTS or Ubuntu and is positioned as a way to avoid recurring cloud GPU costs.

Hacker News · AI · 6d agoAI industryHN 44↑ · 27 comments

NIST wants to overhaul its vulnerability database for the AI age

NIST issued a Federal Register RFI seeking public input on overhauling the National Vulnerability Database for AI-scale, machine-consumable security data.

NIST published a request for information arguing the National Vulnerability Database must adapt as LLMs increasingly find and exploit vulnerabilities at machine scale. The RFI seeks input on integrating automation into vulnerability reporting, faster dissemination to defenders, and transparency and auditability in AI-driven decisions. It follows the White House-backed Gold Eagle clearinghouse at Treasury and the VINCE program with Carnegie Mellon's Software Engineering Institute for AI-discovered vulnerability reports.

CyberScoop · Aug 11, 2026Policy & legal

I've factored the RSA keys of a Certificate Authority from the 90s

Security researcher factored two 512-bit RSA root CA keys from defunct 1990s certificate authority E-Certify using CADO-NFS on a desktop in roughly 30 hours each.

A researcher extracted legacy root certificates from archived Netscape and Internet Explorer installers, identifying two 512-bit RSA roots shipped with Netscape 4.51 in 1999 by the defunct Canadian CA E-Certify. Using CADO-NFS on a Ryzen 9 5950X desktop, the keys were factored in 32 and 29 hours respectively, allowing private key reconstruction. The work comes shortly after RSA-260 (862-bit) was factored, the largest known factorization to date. The researcher also built a legacy TLS server and published keys and tools on GitHub.

Hacker News · security · 9d agoResearchHN 411↑ · 86 comments

AD Rights Management Service (Part 1): Architecture, Deprecation, and Reconnaissance

Huntress maps AD RMS architecture and recon paths, setting up an offline key-extraction attack against its unrotatable 255-year root key.

Huntress's multi-part research examines Active Directory Rights Management Services, Microsoft's enterprise DRM role that still ships in Windows Server 2025 despite migration guidance favoring Azure Information Protection. Part 1 covers the trust model and how an ordinary domain account can locate RMS clusters and read rights-policy templates. Part 2 will detail four independent paths for extracting the Server Licensor Certificate (SLC) private key via service-group membership and the configuration database, enabling fully offline decryption of protected documents. The SLC certificate is valid from 2002 to 2258 and has no key-rotation mechanism, so a stolen key decrypts protected content indefinitely.

Huntress · 9d agoResearch1

Data from drones in Ukraine is fueling a new Wild West marketplace

Ukraine's defense ministry opened millions of battlefield drone data points to over 100 companies, fueling a fast-growing AI training data marketplace.

Ukraine's Ministry of Defense announced in January it would make millions of data points from tens of thousands of drone flights available to military contractors and commercial companies, with more than 100 companies and the UK government gaining access. Enabled Intelligence says it has processed over 500,000 hours of Ukrainian drone footage for use in future AI training. The article argues this creates a commercial battlefield-data marketplace with risks including lost training-data provenance, an extractive economy benefiting wealthier countries, and a governance vacuum requiring international rules.

MIT Technology Review · AI · 12d agoAI industry1

Notes on gotchas while migrating 35kb preprompts from Opus to self-hosted Ollama

Opinion piece urges migrating 35KB preprompts from Anthropic/OpenAI to self-hosted Ollama, citing session privacy risks and safety filters blocking security research.

The author documents gotchas migrating 35KB preprompts from Claude Opus to self-hosted Ollama, motivated by fears that frontier providers train on user sessions, citing the OpenAI Navier-Stokes controversy. The piece argues inference providers cannot audit their own retention or training pipelines and that only self-hosted hardware offers verifiable privacy. It also criticizes frontier safety filters for refusing vulnerability research tasks and calls for models that support exploitability testing in CI/CD pipelines.

Hacker News · AI · 2d agoAI industryHN 41↑ · 13 comments1

The Rise of the Forward Deployed Engineer — and How To Do the Job Right

Palantir veteran Vinoo Ganesh traces the forward deployed engineer role and shares practices for building effective FDE teams.

Kepler CEO and former Palantir forward deployed engineer Vinoo Ganesh argues that labs, startups, and PE firms hire FDEs without a shared definition of the role. He recounts Palantir's Project Frontline rotation, which trained about 250 software engineers as FDEs, many now leading forward deployed teams at OpenAI, Anthropic, xAI, and Anduril. A 2013 failure of the Phoenix transaction store at a bank, where real-world data gaps caused roughly 2.3 million keyspaces and an out-of-memory crash, illustrates why FDEs must own the gap between design and production reality. At Kepler he places the FDE function inside product rather than sales.

Latent Space · 4d agoAI industry 4 sources1

Training a 3.8B LLM to 0.384 CORE for $998 – Hugo Vergnes

Independent developer Hugo Vergnes trained a 3.8B-parameter Llama-style model to 0.384 CORE on 65B tokens for $998 in 43 hours on rented B200s.

Hugo Vergnes trained little-lm, a 3.848B-parameter decoder-only LLM, on 65.3B tokens in 43 hours for $998 using rented NVIDIA B200s, scoring 0.384 on the CORE benchmark and beating nanochat d32 (0.310) at similar cost. The Llama-style architecture uses RMSNorm, RoPE, GQA with 24 query and 8 KV heads, relu-squared MLPs, QK-norm, and ResFormer-style value embeddings that account for 19% of parameters. Key wins included the Muon optimizer for matrix parameters, a trapezoidal learning-rate schedule with linear cooldown, FP8 training plus vocabulary padding for roughly 33% throughput gains, and the ClimMix dataset over FineWeb-Edu. The project, inspired by Karpathy's nanochat, was built as a config-driven YAML framework for small LLM training.

Hacker News · security · 7d agoAI researchHN 28↑ · 2 comments1