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Israel Is Running a Synthetic Think Tank to Influence AI Search Results

Israel funds the Hanover Institute, an AI-generated think tank by ad firm Piro, publishing 100+ articles to influence LLM chatbot answers about Israel.

The Hanover Institute for Public Policy, run by American ad firm Piro Inc and paid for by Israel through HAVAS Media and the state ad agency LaPam, has published more than 100 articles in under a month. The AI detection tool Pangram found tested articles entirely AI-written except their bibliographies, and the site includes an llms.txt file to make it easier for LLMs to scrape. FARA filings show a $900,000 project invoice plus a $100,000 payment, and Piro advertises an 'AI Story Optimization' service for shaping chatbot answers.

404 MediaAug 25, 18:35 UTC · 22d agoAI safety & security

MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes

MetroLLM-Bench is a 955-case benchmark testing language models as transit kiosk tool-calling runtimes across six real metro systems.

The benchmark covers 37-414-station metro systems and eleven task categories including routing, fare calculation, disruptions, accessibility, and adversarial input, with 14 deterministic and 8 semantic scoring components. Of 26 models from six vendors, a PEFT-tuned 4B Qwen 3.5 student scored 91.3 on Tier 1, exceeding GPT-5.6 (90.6/90.0), while Muse Glimmer 30B led the composite ranking. A deterministic rule-based baseline reached 84.6, and PEFT gains over base models shrank from +7.03 points at 2B to -0.91 at 27B.

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

Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.

Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:31 UTC · 7d agoAI research

llm-gemini 0.34

llm-gemini 0.34 adds support for Google's new Gemini 3.8 Flash model with configurable low, medium and high thinking levels.

Simon Willison released llm-gemini 0.34, a plugin for the LLM CLI that adds the gemini-3.8-flash model, including low, medium and high thinking levels, and fixes an issue where async responses failed to record the resolved model version. The release coincided with Google's launch of Gemini 3.8 Flash, plus a Gemini 3.8 Flash Cyber variant restricted to trusted defenders. Willison noted the Flash tier's speed, low cost and competence at HTML and JavaScript generation tasks.

Simon WillisonSep 2, 16:39 UTC · 14d agoAI tools & infra

Akamai Valkey Managed Database: Real-Time Memory for Enterprise AI

Akamai launched Valkey Managed Database, a low-latency in-memory data layer aimed at cutting AI inference costs and accelerating RAG.

Akamai introduced Valkey Managed Database, a managed in-memory data service based on the open-source Valkey project. The company positions it as real-time memory for enterprise AI, optimizing inference costs, accelerating retrieval-augmented generation, and powering real-time AI agents.

Akamai BlogAug 18, 13:00 UTC · 29d agoAI tools & infra

ConvMem: Convolutional Memory for Long-Context Reasoning

Researchers propose ConvMem, a training-free framework treating LLMs as convolutional kernels for parallelizable long-context reasoning beyond fixed context windows.

ConvMem reformulates long-context reasoning as a hierarchical convolution in which the LLM summarizes text segments hierarchically, shortening the reasoning path from a linear chain to a logarithmic tree. It uses configurable strides, skip connections, and multi-kernel convolution to capture evidence, decompose queries, and enable massive parallelization across segments and reasoning threads. On RULER-HotpotQA and RULER-2WikiMultiHopQA it outperforms training-free baselines and avoids the out-of-distribution overfitting seen in RL-trained approaches like MemAgent.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:53 UTC · 7d agoAI research

LLMs and Contextual Integrity

Bruce Schneier highlights two papers: the CIMemories benchmark shows frontier LLMs leak memory attributes up to 69%, and an RL method reduces inappropriate disclosures.

Bruce Schneier discusses contextual integrity in LLMs, referencing the CIMemories benchmark, which uses synthetic profiles with 100+ attributes per user to test whether models with persistent memory disclose sensitive information appropriately. Evaluation showed frontier models exhibit up to 69% attribute-level violations, with GPT-5's violation rate rising from 0.1% to 9.6% across 40 tasks and reaching 25.1% with repeated prompting, showing unstable leakage behavior. A second paper introduces a reinforcement learning framework trained on a synthetic 700-example dataset that substantially reduces inappropriate disclosure while maintaining task performance, with improvements transferring to the human-annotated PrivacyLens benchmark.

Schneier on SecurityAug 18, 10:40 UTC · 29d agoAI safety & security1

OpenBMB Releases MiniCPM5-2B: A 2.52B Dense Model Averaging 53.9 Across 34 Benchmarks and Built to Run On Device

OpenBMB released MiniCPM5-2B, a 2.52B-parameter Apache 2.0 on-device model averaging 53.9 across 34 benchmarks, ahead of Qwen3.5-4B.

OpenBMB released MiniCPM5-2B, a 2,516,756,480-parameter dense LlamaForCausalLM model with grouped-query attention and a 131,072-token context, under Apache 2.0, runnable via vLLM, SGLang, llama.cpp, and Ollama. It averages 53.9 across 34 benchmarks versus 51.1 for Qwen3.5-4B, with strong tool-use (97.1 on tau2-Bench Telecom) and code results (69.1 LiveCodeBench v6, 46.4 SWE-bench Verified). Training combined 400B tokens of deep-thinking SFT, critic-based JustRL II RL teachers, and on-policy distillation merging 16 RL experts; datasets and intermediate checkpoints were published alongside the weights.

MarkTechPostSep 7, 19:18 UTC · 9d agoModel release

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 papersSep 8, 20:00 UTC · 8d agoAI research

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.CLSep 9, 17:59 UTC · 7d agoAI research2

llm 0.34

Version 0.34 of Simon Willison's llm CLI adds response-duration metrics to log output, plus bug fixes and faster log querying.

The open-source llm command-line tool for interacting with large language models released version 0.34. The headline change adds response duration in milliseconds and human-readable form to llm logs --usage Markdown output, plus a new duration_ms field in llm logs --short. The release includes several contributed bug fixes and a significant performance improvement to llm logs, alongside the related llm-openrouter 0.7.1 release.

Simon WillisonSep 2, 19:23 UTC · 14d agoAI tools & infra1

Litelm: LiteLLM Without the Bloat

New open-source project Litelm launched on GitHub as a leaner alternative to the LiteLLM multi-provider LLM gateway.

Litelm is a community-published GitHub project positioned as a stripped-down alternative to LiteLLM, a widely used gateway/SDK for routing requests to multiple LLM providers. The post drew modest attention on Hacker News with 45 points and 17 comments. The listing provides no further technical details, benchmarks, or feature specifics.

Hacker News · AISep 11, 18:10 UTC · 5d agoAI tools & infra HN 45↑ · 17 comments2

TempCloze: Can Video-LLMs Identify the Missing Middle?

TempCloze benchmark tests Video-LLMs' temporal reasoning with 1,521 videos, finding temporal alignment is the primary failure mode across 31 models.

TempCloze is a video cloze benchmark in which models must identify the true missing middle clip given the beginning and ending clips, using 1,521 carefully filtered videos from seven sources, mostly long-take and egocentric footage. Distractors are constructed along three dimensions: Semantic, Alignment and Progression, with shared scenes and objects to reduce appearance cues. Evaluation of 10 proprietary and 21 open-source Video-LLMs found Alignment is the primary bottleneck, with models often recognizing plausible semantics and local event progression but struggling with temporal alignment.

Hugging Face daily papersAug 31, 20:00 UTC · 16d agoAI research

Speculative Decoding in vLLM on AMD GPUs

vLLM benchmarks speculative decoding on AMD Instinct MI300X and MI355X GPUs across five drafting methods including EAGLE-3 and native MTP.

The vLLM project documents draft-and-verify speculative decoding support for AMD GPUs via ROCm, comparing native MTP, Gemma 4 MTP, EAGLE-3, DFlash, and DSpark drafting approaches. Output-token throughput effects varied with drafting method, proposal length, model family, draft checkpoint, workload, and acceptance behavior. The post also covers how to enable each method plus practical tuning and observability considerations.

Hacker News · AISep 7, 09:26 UTC · 9d agoAI tools & infra HN 141↑ · 52 comments1

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-securitySep 10, 03:35 UTC · 6d agoVulnerability

SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models

SimpleMemVLA passes full timestamped video history straight to a VLA backbone, setting state of the art on four memory benchmarks.

SimpleMemVLA is a vision-language-action model for long-horizon manipulation that removes the dedicated memory module entirely. It keeps sampled history intact and feeds it to the backbone as timestamped video, with the hidden states of a generated sub-task serving as the only channel into a standard flow-matching action head. Prefilling the shared history prefix during action execution keeps latency close to a single-frame VLA. The system sets a new state of the art on four memory benchmarks and outperforms retrieval, compression and recurrent-state mechanisms, with causal interventions confirming the policy genuinely reads its history.

Hugging Face daily papersSep 1, 20:00 UTC · 15d agoAI research

I accidentally turned LLM memory into program analysis

A pwning.systems write-up describes how LLM memory functionality was unexpectedly repurposed into a program analysis technique.

A security research post on pwning.systems describes the author's discovery that LLM memory behavior effectively functioned as program analysis. The write-up is hosted on a security-focused blog and surfaced via a security-tagged link aggregator. Detailed technical content is not included in this feed, limiting verifiable specifics.

Lobsters · securityAug 29, 03:16 UTC · 19d agoResearch1

CISO’s CTEM Evaluation Checklist

Horizon3.ai publishes a five-question CISO checklist demanding proof of exploitability from CTEM vendors.

Horizon3.ai released a CISO's CTEM Evaluation Checklist offering five questions for evaluating Continuous Threat Exposure Management technologies, covering proof of exploitability, demonstrated impact, remediation verification, and long-term reduction of exploitable exposure. The checklist warns against relying on scanner findings, risk scores, closed tickets, and isolated test results, urging evidence from the buyer's own environment. It is downloadable vendor marketing material.

Horizon3.aiSep 16, 16:55 UTC · 10h agoIndustry 2 sources

CVE-2026-57822: Apache Artemis, Apache ActiveMQ Artemis: Message-based management parameter deserialization may lead to denial of service

Apache fixed CVE-2026-57822, a deserialization flaw in ActiveMQ Artemis management messaging letting authorized clients trigger broker denial of service.

Apache disclosed a deserialization issue in message-based management request handling for Apache Artemis and Apache ActiveMQ Artemis. It affects Apache Artemis (artemis-core-client) 2.50.0 through 2.56.0 and ActiveMQ Artemis (artemis-core-client) 1.3.0 through 2.44.0. Exploitation requires an authenticated client authorized with MANAGE permission using management-via-messaging; parameter processing can trigger denial of service. Apache rates the issue as important.

oss-securitySep 10, 00:44 UTC · 7d agoVulnerability 7 sources CVE-2026-57822

CVE-2026-67211: Apache OpenNLP: OOM DoS via Unbounded Array Allocation in SymSpellModelSerializer

Apache OpenNLP CVE-2026-67211: unbounded map pre-sizing in SymSpellModelSerializer enables out-of-memory denial of service in opennlp-symspell 3.0.0-M4/M5.

CVE-2026-67211 affects the opennlp-symspell artifact in versions 3.0.0-M4 and 3.0.0-M5 only. The SymSpellModelSerializer.create() method performs unbounded map pre-sizing, allowing crafted input to trigger out-of-memory denial of service. Releases 1.x and 2.x are unaffected because the spellcheck extension was introduced in 3.0.0-M4; the issue is resolved in 3.0.0-M6.

oss-securitySep 11, 19:22 UTC · 5d agoVulnerability 2 sources CVE-2026-67211

Drowning in CVEs and thirsty for answers? Try CTEM

Sponsored Register piece argues traditional vulnerability management cannot scale with CVE volume and promotes Continuous Threat Exposure Management via Horizon3's NodeZero.

The sponsored article cites surging CVE volumes, CVSS triage shortcomings, NVD backlog, and AI-driven discovery accelerating an asymmetric vulnerability cycle. It outlines Gartner's five CTEM steps - scoping, discovery, prioritization, validation, and mobilization - and describes how Horizon3's NodeZero automated pentesting validates exploitable attack paths with evidence. Horizon3 says NodeZero uses a deterministic machine learning expert system rather than general LLMs, limiting generative AI to scoped tasks via AWS Bedrock.

The Register · SecuritySep 3, 15:00 UTC · 13d agoIndustry

ZDI-26-639: Oracle VirtualBox VMSVGA Heap-based Buffer Overflow Local Privilege Escalation Vulnerability

ZDI disclosed a heap-based buffer overflow in Oracle VirtualBox's VMSVGA component (CVE-2026-71116) enabling local privilege escalation.

Zero Day Initiative published ZDI-26-639, a CVSS 7.5 heap-based buffer overflow in the VMSVGA component of Oracle VirtualBox. Local attackers who already execute high-privileged code on the guest system can leverage the flaw to escalate privileges on affected installations. The vulnerability is tracked as CVE-2026-71116. No exploitation is reported.

ZDI Published AdvisoriesSep 9, 05:00 UTC · 7d agoVulnerability 6 sources CVE-2026-71116

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 OnlineSep 9, 10:00 UTC · 7d agoAI safety & security1

ZDI-26-586: OriginLab Origin Viewer OGM File Parsing Memory Corruption Remote Code Execution Vulnerability

ZDI advisory ZDI-26-586 reports a memory corruption RCE (CVE-2026-19886, CVSS 7.8) in OriginLab Origin Viewer OGM file parsing, needing user interaction.

The Zero Day Initiative published advisory ZDI-26-586 describing a memory corruption vulnerability in OriginLab Origin Viewer's OGM file parsing. Exploitation allows remote code execution when a user opens a malicious file or visits a crafted page. ZDI rated the issue CVSS 7.8 and assigned CVE-2026-19886.

ZDI Published AdvisoriesAug 24, 05:00 UTC · 23d agoAdvisory CVE-2026-19886

AI SIEM Search

Huntress launched AI Search for Managed SIEM, translating plain-English questions into ESQL log queries with schedulable custom alerts.

Huntress introduced AI Search in its Managed SIEM, letting users query logs in natural language instead of writing ESQL, KQL, or SPL. The feature generates a real ESQL query behind the scenes, exposes it for users to inspect and learn from, and allows saved searches to be scheduled as recurring alerts at hourly, daily, or weekly cadence. A planned next version will return summarized plain-language answers instead of raw logs.

HuntressSep 7, 14:00 UTC · 9d agoTools

USN-8772-1: AOM vulnerabilities

Ubuntu USN-8772-1 patches four libaom flaws (CVE-2026-56208 to CVE-2026-56211) that could cause heap overflow, arbitrary memory writes, or code execution.

Ubuntu Security Notice USN-8772-1 fixes a heap buffer overflow in libaom's first-pass statistics buffer handling in Look-Ahead Processing mode (CVE-2026-56208), potentially causing denial of service or arbitrary code execution. Three additional flaws in spatial and temporal layer ID validation in the SVC encoder controls (CVE-2026-56209, CVE-2026-56210, CVE-2026-56211) allow arbitrary memory writes, out-of-bounds heap reads, or code execution. Users should apply the updated packages.

Ubuntu Security NoticesSep 16, 09:29 UTC · 17h agoAdvisory CVE-2026-56208 CVE-2026-56209 CVE-2026-56210+1 CVEs

[vim-security] Ex Command Injection in sign_jump() in Vim < v9.2.1090

Vim sign_jump() before v9.2.1090 permits Ex command injection via unescaped buffer names; low-severity patch disclosed by Christian Brabandt.

Christian Brabandt disclosed an Ex command injection vulnerability in Vim's sign_jump() function affecting versions before v9.2.1090, caused by improper neutralization of unescaped buffer names. The issue is rated Low severity and maps to CWE-88 (argument injection) and CWE-94 (code injection). A CVE has been requested but not yet assigned.

oss-securitySep 12, 17:53 UTC · 4d agoVulnerability

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 · securitySep 10, 02:04 UTC · 7d agoAI research HN 28↑ · 2 comments1

MemSentry: A Framework for Detecting Persistent Memory Poisoning in Agentic AI

MemSentry intercepts persistent-memory writes in agentic AI to catch memory poisoning, reaching 91.7% accuracy with SBERT+LR classification.

Memory poisoning lets adversaries plant crafted content in an agent's long-term memory to suppress security alerts, enable privilege escalation, or override policies without modifying model weights or system prompts. The paper presents MemSentry, a configuration-driven framework that evaluates proposed persistent-memory writes on source trust, semantic risk, attack radius over a dependency DAG, access risk, and a signed security-state delta to issue deterministic Accept, Review, or Quarantine decisions. Across 1,000 GPT-4-generated scenarios on a 20-asset dependency DAG, SBERT+LR achieved 91.7% accuracy and 0.908 macro-F1, all four classifiers detected 100% of external quarantine-class threats, and verified-insider writes are escalated for human review rather than auto-quarantined.

arXiv cs.CRSep 8, 13:40 UTC · 8d agoAI safety & security

VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification

VDiff-Bench, a 1,756-question benchmark, shows multimodal LLMs struggle with fine-grained image-difference identification, scoring as low as 8.7% on low-level changes.

VDiff-Bench is a multiple-choice benchmark of 1,756 four-way questions over image pairs covering 10 change categories including position, motion, color, texture, OCR/text and illumination, with curated hard negatives. Evaluation of 11 state-of-the-art open- and closed-source MLLMs shows fine-grained visual comparison remains brittle: 7-8B-scale open-source models score 52.5-70.6% on semantic changes but only 8.7-33.3% on low-level changes like noise and texture. Notably, Grok 4.3 shows a sharp performance drop on noise and texture differences, falling behind large open-source models like Kimi K2.5 and K3.

Hugging Face daily papersSep 4, 20:00 UTC · 12d agoAI research

ZDI-26-591: NVIDIA TensorRT ONNX File Parsing Heap-based Buffer Overflow Remote Code Execution Vulnerability

ZDI disclosed a heap-based buffer overflow RCE (CVE-2026-24272, CVSS 7.8) in NVIDIA TensorRT ONNX parsing, requiring user interaction to exploit.

The Zero Day Initiative published advisory ZDI-26-591 covering a heap-based buffer overflow in NVIDIA TensorRT's ONNX file parsing. Successful exploitation allows remote code execution when a user opens a malicious ONNX file or visits a crafted page. ZDI rated the vulnerability CVSS 7.8 and assigned CVE-2026-24272.

ZDI Published AdvisoriesAug 24, 05:00 UTC · 23d agoAdvisory CVE-2026-24272