Search: “analytics”

When scanners miss the attack: how Cloudflare Client-Side Security protects storefronts

Cloudflare's Page Shield ML uncovered four malicious JavaScript campaigns on storefronts, including affiliate fraud and a remote-backdoor script, that VirusTotal and URLScan missed.

Cloudflare's Page Shield ML detected four client-side JavaScript operations (eight payloads) in live traffic on online storefronts, enabling affiliate commission hijacking, clickless affiliate theft via hidden iframes, user tracking with a remote-code backdoor, and cloaking of paid mobile visitors. Seven of the eight payloads were absent from VirusTotal and URLScan returned no malicious verdict for any, including a Lnkr-family payload indexed unclassified for roughly 2.5 years. Detection relies on a graph neural network over JavaScript syntax trees, an LLM second opinion on Workers AI, and a frontier-model ensemble voting across benign, magecart, other malware, and cryptomining labels.

Cloudflare Blog·20:06 UTC · 3h agoMalware in the wild

How to connect AI usage to business value

OpenAI explained how ChatGPT Admin Console analytics link AI usage, spend, and Codex contributions to business outcomes.

OpenAI published guidance describing analytics features in the ChatGPT Admin Console that combine usage, credit, and token data across ChatGPT Work and Codex. The Insights task classifier groups messages into use cases such as software engineering and sales research, while an Outcomes view tracks Codex contributions to merged commits and lines of code. An Admin plugin and Admin API let teams automate reporting and combine AI analytics with business metrics like ticket resolution time or revenue.

OpenAI News·12:00 UTC · 11h agoAI industry

Cybersecurity jobs available right now: March 10, 2026

Help Net Security's roundup lists open cybersecurity roles at BioNTech, AIG, ServiceNow and others across Europe, the Middle East and Canada.

A job-board roundup of cybersecurity openings including Associate Director Application Security at BioNTech (Germany), CISO at AIG (Israel), Cloud Security Professional at ServiceNow (Italy), and SOC/GRC, analyst, engineer and data governance roles in the UK, UAE, India, Canada and France. Roles span application security, cloud security, SOC operations, compliance and OT environments. Most listings are marked no longer accepting applications.

Help Net Security·Aug 28, 09:06 UTC · 19d agoIndustry

Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:57 UTC · 6d agoAI research

Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection

Unit 42 clusters behavior of 40,000+ AWS identities from 125 cloud environments to map functional roles and enable lightweight SQL-based detection.

Palo Alto Unit 42 built an unsupervised behavioral clustering model using UMAP and HDBSCAN on AWS CloudTrail logs to map cloud identities to functional roles such as administrators, backup services, security tooling and DevOps. The study analyzed over 40,000 identities across 125 cloud environments over two months. The researchers show that heuristics extracted from the clustering map can be implemented in standard SQL, enabling role classification at scale without running a continuous ML pipeline. The methodology extends to audit logs from other cloud providers, SaaS and Kubernetes.

Palo Alto Unit 42·Sep 14, 10:00 UTC · 2d agoResearch

Recorded Future Launches Impact and Metrics Dashboard

Recorded Future releases an Impact and Metrics Dashboard aggregating risk-reduction, detection, and analyst-efficiency metrics for customer leadership reporting.

The dashboard pulls data from a customer's environment, alerts, integrations, threat detections, and analyst activity into six metric areas: platform-wide security value, threat prioritization, threat detection, digital risk protection, account and credential monitoring, and Recorded Future AI and Insikt Group research usage. It is available now to all Recorded Future customers, who are advised to configure Priority Intelligence Requirements in Settings so reporting maps to their intelligence program. The vendor cites its 2025 ROI Report across nearly 300 customers reporting 351.3% annual ROI and says customers aligning alerting to PIRs identified new threats 65% faster.

Recorded Future·Aug 24, 00:00 UTC · 23d agoTools

Multiple Vulnerabilities Discovered in a SCADA System

Unit 42 details five vulnerabilities (CVSS 7.0-7.8) in ICONICS Suite SCADA software enabling privilege escalation and DoS.

Unit 42 discovered five vulnerabilities (CVE-2024-1182, CVE-2024-7587, CVE-2024-8299, CVE-2024-8300, CVE-2024-9852) in ICONICS Suite versions 10.97.2 and earlier for Windows during a 2024 security assessment. The flaws, rated CVSS 7.0-7.8, allow DLL hijacking, privilege escalation, information disclosure, denial-of-service and potentially full system compromise. ICONICS Suite is a SCADA solution with hundreds of thousands of installations in over 100 countries, widely used in critical infrastructure, and several dozen servers are internet-exposed per Unit 42 telemetry. ICONICS released patches and advisories with workarounds in 2024.

Palo Alto Unit 42·Aug 19, 12:04 UTC · 28d agoVulnerabilityCVE-2024-1182CVE-2024-7587CVE-2024-8299+2 CVEs1

MasterControl Seventeen Every Time

Governed enterprise analytics study shows deterministic policy execution matched 110/110 answer-and-evidence contracts while runtime agent planning matched none.

The paper studies a governed approach where a language model interprets the question while deterministic policy selects and runs a pre-approved analytical program returning results and evidence. Across 440 runs, three 8B models generated SQL and selected tools at runtime, while Qwen3-8B only interpreted intent and policy executed the approved program. None of 330 runtime-planning episodes satisfied the full answer-and-evidence contract, whereas the policy-executed analyzer matched 110 of 110. The authors note this is configuration-specific and expressiveness is preserved via relational operations, aggregation, comparison, windows, ranking, and similarity with replayable results.

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

Why 2026 is the Year to Upgrade to an Agentic AI SOC

Elastic Security Labs argues 2026 is the production inflection point for agentic AI in security operations centers.

Elastic Security Labs argues 2026 is the practical inflection point for agentic AI SOCs, noting nearly two-thirds of organizations are experimenting with AI agents while fewer than one in four have production deployments. The piece outlines operational challenges and recommendations: treat agents as non-human identities with least-privilege tool access, version-control system prompts as code, deploy unified agents with on-demand task packages, and enforce per-agent budgets and rate limits. It stresses explainability via RAG and transparent reasoning traces so analysts can verify and override autonomous decisions.

Elastic Security Labs·Sep 8, 21:00 UTC · 8d agoIndustry

The Frontier AEO Tracker: What Astra Chooses (and every other frontier model, and what you can do about it)

Latent Space launches an AEO tracker scoring 7 frontier models' product recommendations across 161 categories, revealing generational bias flips.

Latent Space built a tracker measuring Answer Engine Optimization by running 6 prompt variations across 7 frontier models with search enabled over 161 product categories, scoring first choices, alternatives, mentions, and anti-recommendations. It found 28 categories with a universally dominant primary choice and observed soft biases, such as models favoring their own lab's coding agents. Analysis of Anthropic's Sol→Astra and Opus→Fable generations showed newer models consulting fewer sources and being less likely to change answers when questions are paraphrased.

Latent Space·Sep 7, 21:32 UTC · 9d agoAI research

The Coding-Agent Trap: When a "Free" LLM Endpoint Is the Adversary, (Mon, Aug 31st)

A SANS honeypot caught a real coding-agent session routed to a rogue "free" LLM endpoint, exposing a Windows user's transcript and tool outputs.

A SANS analyst describes how an internet-exposed inference honeypot was discovered, relabeled with sought-after model names like DeepSeek, and enrolled in infrastructure serving "free" LLM backends. On 2026-08-30 an opencode terminal coding agent sent an 88-message, 224 KB transcript 210 times in 91 seconds via a China Unicom relay, exposing directory listings, tool outputs and read file portions. The analyst frames tool-enabled agents treating model endpoints as trusted control planes as a novel risk — a "rogue model endpoint" that could request tool executions on the user's machine.

SANS Internet Storm Center·Aug 31, 20:00 UTC · 16d agoAI safety & security1

New insights from Google’s AI & Economy ATLAS

Google launches an interactive AI & Economy ATLAS experience; new research shows nearly half of surveyed scientists use AI daily.

Google introduced new interactive, open-access data visualizations for its AI & Economy ATLAS project tracking global AI adoption patterns. Research from Google, Google DeepMind, and MIT FutureTech analyzed 2,600 specialized AI models and surveyed over 600 U.S. and U.K. scientists, finding nearly half use AI daily and report saving almost seven hours per week. The study also found validation bottlenecks and a growing backlog of untested hypotheses limiting research productivity gains.

Google · AI·Sep 15, 13:00 UTC · 1d agoAI industry

Risky Bulletin: Slovakia finds Russian backdoor in traffic speed cameras

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

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

Risky Business News·Aug 19, 04:52 UTC · 28d agoThreat actor in the wild1

Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation

Benchmark Radar provides a living searchable database of 1,283 AI benchmark records and 12,916 score observations drawn from 37 daily discovery sources.

Benchmark Radar combines daily discovery of benchmark papers, repositories, datasets, and releases from 13 direct connectors and 24 first-party feeds into a searchable catalog with model card mentions and score histories. The catalog contains 1,283 source records drawn from 4 benchmark catalogs plus 12,916 numeric observations on 790 records. The release includes a web dashboard with leaderboard, Pareto frontier of score versus usage, saturation and trend views, daily feeds, a CLI, and reproducible analysis. The paper audits the full catalog and examines benchmark saturation and limits of score comparisons.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoAI research

Top 10 Best Data Security Posture Management (DSPM) Tools in 2026

A 2026 scorecard ranks DSPM tools with Wiz and Cyera tied first, documenting consolidation via Palo Alto, Rubrik, Proofpoint, and CrowdStrike acquisitions.

The article ranks ten DSPM platforms: Wiz and Cyera tie at 8.7/10, followed by BigID at 8.5 and Securiti at 8.4, scored on discovery breadth, classification accuracy, access context, remediation, and value. It highlights heavy market consolidation, noting Dig Security was acquired by Palo Alto Networks, Laminar by Rubrik, Normalyze by Proofpoint, and Flow Security by CrowdStrike. Buyers are advised to purchase from current owners and confirm post-acquisition integration state.

Cyber Security News·07:29 UTC · 15h agoIndustry1

Show HN: LLM Attention Visualization

A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.

A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.

Hacker News · AI·Sep 8, 16:59 UTC · 8d agoAI tools & infraHN 41↑ · 9 comments

U.S. Disrupts Xinbi Guarantee Scam Marketplace, Freezes $52.8 Million in Crypto

US DOJ and Treasury disrupt Xinbi Guarantee Telegram scam marketplace, sanctioning it and freezing $52.8M in USDT across 52 wallets.

The DOJ seized Xinbi Guarantee's Telegram channels and cryptocurrency wallets while OFAC sanctioned the marketplace, freezing $52.8 million in USDT from 52 wallets and bringing the Scam Center Strike Force's total restrained funds to roughly $938 million. Elliptic, which worked with the Secret Service, estimates Xinbi has processed $30 billion in transactions since around 2022, serving pig-butchering scam operators and links to North Korean hackers, Jin Bei Group, and Prince Group TCO. The strike force dismantled 13 scam compounds in Madagascar, seizing over 3,200 devices and interviewing roughly 400 arrestees, with about 30 Chinese compound leaders repatriated to China. After Tether froze funds, Xinbi began converting remaining USDT into the USDD stablecoin.

The Hacker News·Sep 9, 18:26 UTC · 7d agoPolicy & legal

18 ways to check whether data can be trusted for AI

ETSI published TR 104 180 defining 18 data quality metrics, including fairness and privacy, to assess whether datasets are fit for AI.

ETSI's technical report TR 104 180 defines 18 metrics across four groups - intrinsic quality, usability and lineage, fairness, and privacy - each with calculation formulas, plus an open-source tool that scores datasets. Testing on an aircraft engine sensor dataset and a US census dataset revealed a roughly threefold gender gap in high earners (about 31% of men versus 11% of women) and two privacy failures: re-identification via age, race, sex, and country, and sensitive fields stored in plaintext. The working group included Sejong University, EGM, TTA, Daejeon University, and CNIT.

Help Net Security·Sep 7, 00:00 UTC · 9d agoAI policy

Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

Researchers present SMART, an ML performance-modeling library regenerated by AI coding agents from natural-language design docs instead of code.

The paper describes SMART, a symbolic performance-modeling library whose main branch contains almost no code: the repository is a DAG of self-contained design documents, and coding sub-agents regenerate implementations from only the docs on version updates. Reliability rests on a worked-example doc style used as in-context demonstrations and a minimal operator IR with SymPy cost expressions, offering both fast analytical roll-up and fine-grained modulo-scheduling modes. Regenerated implementations reproduce hand-audited reference models, including DeepSeek-V3 serving on a TPU pod slice, to round-off precision.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:08 UTC · 12d agoAI research1

From Concept to Context Engine: How Wiz Built AI-Powered Data Discovery

Wiz details the multi-agent pipeline and feedback loops that evolved its bucket scanner into AI-powered data discovery.

Wiz published an engineering write-up on building its AI-powered data discovery capability, tracing the evolution from a bucket scanner to a context engine. The post explains the multi-agent pipeline and feedback loops behind the system. The article is a vendor engineering deep-dive with no disclosed vulnerabilities, incidents, or exploitation activity.

Wiz Blog·Aug 27, 15:52 UTC · 20d agoTools1

[AINews] GPT-6 Astra: OpenAI’s biggest LLM launch of all time

OpenAI launched GPT-6 Astra, its new flagship model, claiming state-of-the-art computer use, software engineering, math, and cybersecurity capabilities.

OpenAI launched GPT-6 Astra as its new flagship model, describing it as its most intelligent and aligned model with state-of-the-art computer use, software engineering, and math/science capabilities. Pricing is $10/$50 per 1M input/output tokens standard ($20/$100 fast tier), rolling out first to limited organizations, then ChatGPT Plus/Pro/Business/Enterprise, the API, and AWS. OpenAI claims 99.9% on ARC-AGI-3, 98% on FrontierMath Tier 4, and 100% on ExploitBench. Artificial Analysis scored Astra 67 on the Coding Agent Index and 61 on the Intelligence Index, behind Claude Fable 5.1, and the system card drew attention for reporting decreased chain-of-thought monitorability despite alignment gains.

Latent Space·Sep 4, 05:18 UTC · 12d agoModel release3

SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking

SAS trains attention sparsification end-to-end with the language modeling loss, beating sparse attention baselines especially under tight context budgets.

Simple Attention Sparsification (SAS) injects the selector's continuous scores into attention logits in log form inside the softmax, letting gradients from the language modeling loss directly update the ranking of context units. The method uses normalized softmax gates calibrated against the current block and a memory-efficient Triton kernel integrated into FlashAttention-style computation. Across reasoning, long-context, and agentic tasks, SAS consistently outperforms trainable sparse attention baselines across budgets, with the largest gains under tight attention budgets.

Hugging Face daily papers·updated Sep 11, 17:58 UTC · 5d ago·first Sep 10, 20:00 UTC · 6d agoAI research 2 sources1

AI is getting better at election facts, but voters shouldn’t rely on it

CyberScoop·Aug 5, 09:00 UTC · Aug 5, 2026Exploit / PoC in the wild

Evidence-Grounded Retrieval for Investigation Hunt Lead Generation from CTI Reports

AHLERT generates environment-aware threat hunting leads from CTI reports using ATT&CK-seeded knowledge graph retrieval, roughly doubling F1 over flat-RAG baselines.

The paper introduces AHLERT, a system that converts Cyber Threat Intelligence reports into structured, investigable hunt leads via hybrid dense retrieval with multi-hop traversal over an MITRE ATT&CK-seeded knowledge graph and ontology-grounded RAG constrained to the defender's assets. It is LLM-agnostic and evaluated on public CTI reports for well-known APTs across proprietary and open-weight models. Hybrid evidence retrieval with ontology grounding raises mean F1 from 0.44 to 0.85, and AHLERT attains the highest effectiveness score (~86.95%) versus off-the-shelf LLMs.

arXiv cs.CR·Sep 8, 14:21 UTC · 8d agoResearch

Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics

Latent-MoE adds domain-aware mixture-of-experts routing to PINNs, improving accuracy over an order of magnitude on multi-regime physics PDEs.

The paper shows standard coordinate networks used in physics-informed neural networks have translation-variant NTKs causing long-range gradient conflicts on PDEs with spatially varying physics. MoE architectures with centered compact-support routers produce a uniformly banded NTK that localizes learning. Latent-MoE interleaves domain-aware MoE blocks in a shared backbone, outperforming FB-PINNs and X-PINNs by over an order of magnitude on multi-stage time-variable physics benchmarks.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:52 UTC · 9d agoAI research

When companies get specific about AI, revenue growth looks different

A Carnegie Mellon and Larridin study of 564 companies links specific AI disclosures to 8 points higher revenue growth, not margins.

Researchers at Carnegie Mellon University and Larridin analyzed 564 companies across 12 sectors using 478 10-K filings, more than 30,000 classified job postings and an AI maturity tracker. Companies at the top of 'narrative concreteness', meaning they named deployed AI systems with measurable outcomes, were associated with 8.0 percentage points higher year-over-year revenue growth, while no public AI signal predicted operating margins or risk-adjusted stock returns. AI infrastructure suppliers outperformed matched peers by about 32 percentage points over four months. The authors stress the findings show association, not causation.

Help Net Security·Aug 17, 00:00 UTC · Aug 17, 2026AI industry

Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

Real-SWE benchmark tests coding agents on licensed private enterprise codebases; top model Fable 5.1 resolves only 38.8% of tasks.

Real-SWE is a new benchmark evaluating frontier AI coding agents on tasks drawn from private production codebases licensed from real companies, spanning billing, tax calculation, and cross-service migrations. Fable 5.1 with Claude Code leads at 38.8% resolution rate (pass@1 over eight runs), followed by GPT-6 Astra Codex CLI at 33.8% and Gemini 3.8 Flash Gemini CLI at 31.2%. Tasks use native harnesses and realistic tooling including Docker, Kubernetes, PostgreSQL, Redis, and Linear; median reference solutions edit 11 files versus 6 for DeepSWE and FrontierCode.

Hacker News · AI·Sep 12, 20:25 UTC · 4d agoAI researchHN 45↑ · 31 comments

CausalArena: Benchmarking Causal Discovery in the Foundation Model Era

Researchers introduce CausalArena, a unified benchmark revealing that causal discovery rankings shift substantially across structural causal model families and protocols.

The paper presents CausalArena, a unified and evolvable benchmark for causal discovery combining synthetic structural causal models, semantically grounded operational SCMs, formula-grounded scientific SCMs, and public real-world datasets. Experiments across classical, neural, and pretrained causal discovery foundation models show large ranking shifts between benchmark regimes. The authors identify pretraining-evaluation overlap and benchmark diversity as central evaluation challenges.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:53 UTC · 6d agoAI research

I Am No One: Style-Aware Paraphrasing for Text Anonymization

Prompt-driven style-aware paraphrasing with LLMs cuts authorship attribution F1 by 60-70% while preserving content quality.

The paper proposes a style-aware, prompt-driven anonymization approach using pretrained LLMs to build compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. It addresses stylometric re-identification risks in anonymized text, including ASR transcripts of meetings and call-center calls where leakage persists after acoustic anonymization. Across blog and review datasets, the approach reduces authorship attribution F1 by 60-70%, substantially outperforming both DP-based and non-DP baselines while maintaining readability.

arXiv cs.CR·Sep 11, 02:07 UTC · 5d agoResearch

RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments

RegionFed is a gradient-level federated learning framework enabling personalized retail query understanding while matching centralized accuracy with differential privacy.

RegionFed is an architecture-robust federated learning framework for personalized query understanding that operates at the gradient level, using the l2 conflict between regional and global gradients to diagnose heterogeneity and control personalization. Existing parameter-level personalized FL methods collapse on transformers, falling below 10% accuracy on T5, while RegionFed deploys unchanged on T5-Small, T5-3B, RoBERTa, and CNNs. RegionFed-Meta achieves 92.27% across Amazon ESCI, Amazon Reviews, and LEAF-FEMNIST, within 0.23 percentage points of the centralized upper bound, with epsilon-approx-0.60 differential privacy.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:50 UTC · 12d agoAI research1

[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale

DeepSeek released V4.1-Flash, an open-weight 763B-parameter model with a novel causal encoder-decoder architecture, 1M context, vision input, and MIT license.

DeepSeek launched V4.1-Flash, an open-weight MIT-licensed model using a novel causal encoder-decoder architecture with 763B total parameters and asymmetric active parameters: 8B for prefill and 16B for decode. It supports 1M-token context and text+image input, priced at $0.30 per 1M input and $1.20 per 1M output tokens with a 50% off-peak discount. Artificial Analysis scored it 40 on its Intelligence Index, above DeepSeek V4 Pro 0813, and Vals ranked it the #1 open-weight model ahead of Kimi K3. Baseten shipped day-0 support and Ollama began rolling it out to paid subscribers.

Latent Space·Sep 12, 05:56 UTC · 4d agoModel release 4 sources1

Google's new AI model predicts the future from sales data, weather, and discount schedules

Google Research released TimesFM-3, a 330M-parameter multivariate time series forecasting model that tops Gift-Eval, FEV-Bench, and Time benchmarks and is on Hugging Face.

Google Research released TimesFM-3, a 330-million-parameter Transformer-based time series forecasting model trained on more than one trillion real and synthetic data points. It works zero-shot and adds multivariate support, ingesting related series, historical-only covariates, and known future events such as discount schedules and weather forecasts, while filling all future time steps in a single one-shot pass. Google reports first place among pretrained forecasting models on Gift-Eval, FEV-Bench, and Time, ahead of Amazon's Chronos-2, the Toto-2.0 family, and its own TimesFM-2.5. Weights are available on GitHub and Hugging Face, with BigQuery integration planned in the coming weeks.

The Decoder·Sep 12, 09:26 UTC · 4d agoModel release2

Your AI agent's system prompt is not a security control

AWS and SANS guidance says system prompts are not security controls; enforce user permissions at retrieval time and default-deny every agent tool invocation.

AWS VP Gee Rittenhouse and SANS fellow Eric Johnson, with three AWS security specialists, published agentic AI security guidance for organizations with agents running or under development. They recommend scoping queries to user permissions inside existing RBAC/ABAC systems and filtering results before the model's context window, noting prompts can be bypassed, ignored, or overridden. The guidance warns risk concentrates when one agent holds sensitive data access, external communication, and exposure to untrusted content, the vector for prompt injection, which OWASP ranks as the top AI application threat. It cites IBM 2025 research that ungoverned shadow AI added $670,000 to average breach cost, and prescribes 30-day behavioral baselines, Cedar or Open Policy Agent for default-deny tool invocation, and four-layer containment with automatic circuit breakers.

Help Net Security·Sep 7, 12:03 UTC · 9d agoAI safety & security

What the Data Says About AI in Security Operations in 2026

Prophet Security's 2026 survey of 250+ security pros: 40% use AI daily; AI users report 25%+ faster investigations and rising AI-driven attacks.

Prophet Security's State of AI in Security Operations 2026 report, based on a survey of 250+ cybersecurity professionals, found 40% of security teams use AI daily and only 4% have no adoption plans. Teams average 100 daily alerts (up to 1,000 at large firms), leave 28% of alerts uninvestigated, and 60% of respondents said missed alerts led to breaches or downtime. Among AI adopters, 72% report at least 25% faster investigations, 56% observed increased AI-driven attacks, and no respondents grant AI full unsupervised autonomy. Data privacy (44%) and explainability (41%) top the adoption hurdles.

The Hacker News·Aug 27, 11:30 UTC · 20d agoIndustry

ConnectSecure helps MSPs automate Microsoft 365 security remediation

ConnectSecure launches Microsoft 365 Auto Remediation and AI-powered Training Assessments to help MSPs automate M365 security fixes and compliance training.

ConnectSecure's platform now automates remediation of supported Microsoft 365 security findings through conditional access policies, covering gaps such as missing MFA enforcement, legacy authentication and risky sign-ins. A new AI-assisted Training Assessments module generates, assigns and measures security assessments with configurable difficulty, pass thresholds and multilingual support. The previously announced Patch 360 patch management capability, including pilot-first validation, staged rollouts and rollback, is also now available.

Help Net Security·Aug 12, 00:00 UTC · Aug 12, 2026Tools

Intelligence-Driven SOC: Modernizing Threat Monitoring and Detection Engineering for Ultimate MTTR Reduction

ANY.RUN urges SOC teams and MSSPs to adopt intelligence-driven threat monitoring to reduce mean time to respond.

ANY.RUN published a vendor blog post arguing that threat monitoring is the connective tissue of modern security operations and that SOC teams and MSSPs must move from simple log collection to a proactive, intelligence-driven framework. The piece promotes ANY.RUN's Threat Intelligence offering as the solution for detection engineering and MTTR reduction. It is promotional content rather than a threat disclosure or research finding.

ANY.RUN·Aug 12, 07:58 UTC · Aug 12, 2026Industry

Android car head units infected with proxy botnet malware through built-in software updaters

Kaspersky found malware delivered via car head unit updaters, attributed to the MoYu Group's BADBOX operation, recruiting devices into a proxy botnet.

Kaspersky discovered malware delivered through the built-in TWCore system updater in Android-based car head units running DoFun infotainment firmware, turning devices into ad-fraud tools and nodes in a proxy botnet. The three-stage infection chain (JarService dropper, loader, and final payload supporting nine commands) installs the zhima reverse-proxy module, which Nokia's Deepfield team independently found on TV set-top boxes. Kaspersky attributes the operation with high confidence to the MoYu Group, linked to the BADBOX supply-chain botnet first identified by HUMAN Security in 2023. DoFun closed the gap after Kaspersky's responsible disclosure.

Help Net Security·Aug 24, 00:00 UTC · 23d agoMalware in the wild

Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks

Survey of four harness mechanisms—context budgeting, compaction, todo-state, and memory—that keep long-horizon LLM agents on task across 200+ tool calls.

The article details how agent harnesses, not larger context windows, solve context overflow and goal loss on long-horizon tasks, citing Chroma's Context Rot report showing 18 LLMs (GPT-4.1, Claude 4, Gemini 2.5, Qwen3) degrade on long inputs. Concrete implementations include LangChain Deep Agents offloading tool responses over 20,000 tokens to the filesystem and truncating old tool calls at 85% window usage, and Claude Code capping auto memory at 25KB while re-reading the 5 most recently modified files after compaction. OpenAI's Responses API now offers server-side compaction via context_management with a standalone /responses/compact endpoint, which Codex uses for long-running coding tasks. Manus reports a roughly 100:1 input-to-output token ratio per ~50-tool-call task, motivating todo.md state recitation to prevent goal drift.

MarkTechPost·Sep 13, 05:56 UTC · 3d agoAI research1

Knowledgator Releases GLiFormer: A 575M-Parameter Encoder That Hits 91.10 F1 on Nested JSON Extraction Without Generating Tokens

Knowledgator released GLiFormer, an Apache-2.0 encoder (264M/575M) handling NER, classification, relations, and nested JSON extraction, scoring 91.10 F1.

Knowledgator Engineering released GLiFormer, a schema-conditioned encoder that performs NER, classification, relation extraction, nested JSON structuring, and embeddings without generating output tokens. GLiFormer Large v1 has 575.6M parameters and scores 91.10 F1 on nested JSON extraction, close to GPT-5.6-luna's 91.96; both checkpoints are Apache 2.0 on Hugging Face. Reported median latency is 69 ms on GPU for the base model, though relation extraction (21.33 micro-F1) still trails GLiNER-Relex and larger LLMs.

MarkTechPost·21:19 UTC · 2h agoModel release

Intelligence Insights: August 2026

Red Canary's August 2026 Intelligence Insights report covers new threat actor debuts, departures, and threats targeting the blockchain sector.

Red Canary published its August 2026 Intelligence Insights, a monthly threat intelligence roundup. This edition highlights newly emerged threat actors, groups that ceased or reduced activity, and danger on the blockchain, likely threats to cryptocurrency and blockchain ecosystems.

Red Canary·Aug 20, 20:09 UTC · 27d agoThreat actor