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Mapping out your unknown: A threat hunter’s guide to GitHub
Datadog Security Labs publishes a threat-hunting guide with audit-log queries to detect GitHub token theft, device code phishing, and source code exfiltration.
Datadog's threat-hunting guide covers GitHub audit log queries for detecting compromised accounts, stolen personal access tokens, and malicious OAuth app authorizations. Attackers typically obtain credentials through phishing, credential stuffing, leaked secrets, or device code phishing, then map private repositories, exfiltrate source code, and pivot into connected cloud and CI/CD environments. The guide maps detections to MITRE techniques like T1078 and T1528 and documents GitHub logging quirks affecting attribution, token metadata, and visibility fields.
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Evaluating Verified Autonomy in Quantum Engineering
Quantum-Harbor lab and QIQCBench (49 tasks) expose wide performance gaps across 17 frontier agentic systems in verified quantum engineering.
Researchers built Quantum-Harbor, a virtual laboratory providing a controlled execution environment where scientific AI agents interacting with quantum systems can have both actions and conclusions directly verified. QIQCBench contributes 49 expert-authored tasks spanning calibration and control, error correction and compilation, and sensing and networking. Across 17 frontier agentic systems, verified performance varied widely, exposing a substantial gap between demonstrated capability and reliable autonomous operation.
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Globalgig expands managed security portfolio to protect enterprise AI
Globalgig adds managed AI security services covering posture management, access control, runtime protection and 24/7 AI threat monitoring built on Palo Alto Networks.
MSSP Globalgig expanded its managed security portfolio with enterprise AI protection spanning AI Security Posture Management, AI Access Security, AI Runtime Security, and AI Security Operations. The offering cites the 2026 Cost of a Data Breach Report finding that shadow AI was involved in 43% of breaches this year and 92% of AI-related breach victims lacked adequate AI access controls. Services are built on Palo Alto Networks technology, with Orchestra Insight supplying network context.
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MarkSec: Capability-Aware Evaluation of Adversarial Attacks Against LLM Watermarks
MarkSec unifies evaluation of stealing, scrubbing, and spoofing attacks against LLM watermarks with quality-constrained success metrics under shared reporting protocols.
MarkSec is a framework unifying analysis of stealing, scrubbing, and spoofing attacks against LLM watermarks under shared detector calibration, metric definitions, and reporting protocols. It introduces a quality-constrained attack success metric that jointly assesses attack effectiveness and text quality. Experiments across representative watermark families, attacks, LLMs, and datasets show that attacks strongest by watermark removal alone can fall behind general rewriting when success requires acceptable text quality, and stealing-based scrubbers often underperform the best general-scrubbing baselines.
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Illusion of Depth: Revealing Hidden Stereo Vision Vulnerabilities in Depth Estimation
Simple repeating patterns let attackers shift stereo-camera depth estimates by up to 20 meters, triggering emergency braking in autonomous driving frameworks at 40 km/h.
The paper reveals an intrinsic vulnerability in stereo cameras stemming from pixel sampling and calibration processes, letting attackers finely control estimated depth of real obstacles using simple repeating patterns without adversarial ML techniques. The attack was evaluated against BM and SGBM stereo matching algorithms, deep learning models PSMNet, MoCha-Stereo, and UniMatch, the stereo-LiDAR fusion model SGM-DDC, and commercial cameras ZED2 and Intel RealSense D435; on ZED2, obstacles can be displaced up to 20 meters farther or 12 meters closer. A 0.5-second attack triggered emergency braking in a popular autonomous driving framework, with feasibility confirmed at driving speeds up to 40 km/h using CARLA. State-of-the-art defenses proved ineffective, and the authors propose a similarity-score strategy to dynamically detect and suppress depth discrepancies.
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PIDS-Bench: Evaluating Prompt-Injection Detectors Under Over-Defense, Obfuscation, and Distribution Shift
PIDS-Bench shows prompt-injection detectors scoring F1 above 0.98 still misclassify about one-third of external benign security-adjacent prompts, revealing provenance-sensitive over-defense.
PIDS-Bench is a frozen multi-axis benchmark that jointly evaluates prompt-injection detectors on attack detection and benign false-positive behavior at fixed thresholds, spanning in-distribution inputs, hard-benign prompts, obfuscated attacks, and domain/structural distribution shifts. It evaluates seven detectors plus a rule-based lower-bound reference. A detector exceeding F1 = 0.98 on held-out data still misclassifies roughly one-third of an externally-sourced benign security-adjacent subset, and no internal detector reaches F1 >= 0.95 with hard-benign FPR <= 0.10 on the stress distribution. Hard-negative augmentation nearly eliminates over-defense on curated stress inputs but leaves it intact on externally-sourced prompts, a pattern termed provenance-sensitive over-defense.
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E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning
E2A-Bench, a 969-query financial chart reasoning benchmark, finds VLMs fail evidence-to-action consistency, with fine-tuning amplifying BUY:SELL bias 4-6x.
E2A-Bench is a 969-query benchmark built from 323 HS300 constituents across three input modalities with deterministic OHLCV-derived evidence anchors, evaluating grounding, reasoning-action consistency, evidence-confidence calibration, and directional coverage via UCR, RCI, ECI, and NDR metrics. Testing 20 VLMs showed the lowest-hallucination model ranked near the bottom on coverage with only 6.4% directional coverage, and oracle-aided verification reduced unsupported claims but could collapse coverage. Financial fine-tuning amplified the BUY:SELL ratio by factors of 4.21 to 4.68 across base-fine-tuned pairs.
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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.
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SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking
SAS trains a gated sparse attention selector end-to-end with the language modeling loss, beating distillation-based sparsifiers on reasoning, long-context, and agentic tasks.
The paper proposes Simple Attention Sparsification (SAS), which injects a selector's continuous scores into attention softmax logits so the language modeling loss directly optimizes context ranking instead of distilling dense attention distributions. Key design choices include log-form gates inside the softmax, normalized gates calibrated against the current block, and preserved continuous selector scores. A memory-efficient Triton kernel integrates SAS into FlashAttention-style computation for long-sequence training. SAS outperforms trainable sparse attention baselines across budgets, with the largest gains under tight attention budgets.
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Fresh-Challenge VDF Attestations for Model-Relative Response Latency
Fresh-Challenge VDF Attestations bind verifiable delay functions to unpredictable public challenges, yielding succinct evidence of model-relative response latency.
The paper specifies FCLA, a protocol composition that binds a VDF to an unpredictable public challenge, a message, and independently auditable release/receipt records. Under explicit assumptions about VDF sequentiality and a calibrated bound on an adversary's sequential evaluation rate, an accepted transcript is inconsistent with post-challenge generation. A benchmark of the public reference implementation confirms the expected evaluation-versus-verification separation on one documented machine. The contribution is a protocol design analysis, not a new VDF construction.
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A First-Principles Evaluation of Graph-Based Network Intrusion Detection Systems
GIDS-Eval framework reveals evaluation gaps in graph-based network intrusion detection; two crafted edges fully evade three detector-dataset pairs.
Researchers introduce GIDS-Eval, a framework decomposing graph-based network intrusion detection systems into six interchangeable stages to enable controlled comparisons. Surveying nine GIDS and reimplementing five, they find two crafted edges achieve full evasion against three of eight detector-dataset pairs, snapshot windows alone cause a mean 38.3% relative swing in average precision, and none of 18 replayed detector-dataset pairs can alert as events arrive. Their encoder-free GIDS-Lite control ranks first by AP on two of four datasets at up to 575x lower runtime.
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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).
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UK appoints new commander of National Cyber Force
The UK National Cyber Force has a new commander, its third since 2020, succeeding Air Vice-Marshal Tim Neal-Hopes; identity not yet avowed.
The United Kingdom's National Cyber Force has changed commanders, with the new leader's identity not yet formally avowed pending security considerations. The new commander is the third since the force was established in 2020, following James Babbage (GCHQ) and Air Vice-Marshal Tim Neal-Hopes (armed forces), both of whom have now completed their tenure. The NCF consolidates UK offensive cyber capabilities under a joint defense-intelligence partnership including GCHQ, MI6, and Dstl, with funding committed to 2030 and a permanent headquarters at Samlesbury progressing as scheduled.
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The Intelligible World of Agents
Recorded Future argues cybersecurity AI agents perform better when reasoning over structured, curated intelligence graphs rather than fragmented alerts or open-source noise.
In a vendor essay, Recorded Future describes how its security agents produced more authoritative analyses after being re-architected to reason primarily over the Recorded Future Intelligence Graph instead of weighting open-source information equally. The author argues agentic decision quality depends mainly on a structured, current operational world model of assets, vulnerabilities, threat actors, detections and organizational context, not on model intelligence itself. The piece further claims frontier model access is commoditizing and that orchestration tooling will converge, making trusted representations of organizational knowledge the durable competitive differentiator.
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Memory as Plans: World-Action Modeling with Memory-Grounded Planning
Researchers introduce MaP-WAM, decomposing memory-dependent robot manipulation into memory-grounded planning and plan-conditioned execution, achieving 83.3% on RMBench and 78% on real robots.
MaP-WAM converts long-term multimodal episodic memory — segment records with language instructions and sparse visual context — into compact plans of next-segment language goals and visual guidance. A World-Action-Progress model jointly predicts action chunks and execution progress, calibrating predictions via plan-observation alignment for adaptive segment transitions and closed-loop context updates. Structured attention keeps the executor context length fixed and enables key-value caching, yielding state-of-the-art 83.3% success on RMBench, 78.0% on real-robot tasks, and roughly constant inference latency as task history grows.
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Attention-DP3: Spatially Object-aware 3D Diffusion Policy via Geometry-aligned Attentional Conditioning
Attention-DP3 adds spatially object-aware attentional conditioning to 3D diffusion policies, improving robotic manipulation by up to 31% under heavy clutter.
Attention-DP3 injects object-level geometric cues into the unchanged DP3 diffusion policy via Tri-field Attentional Conditioning, using targetness, intra-target saliency, and backgroundness fields. Open-vocabulary 2D segmentation masks are lifted to 3D with calibrated camera geometry to build object-centric priors. Experiments on Adroit, DexArt, MetaWorld, and a real-world SO101 platform show state-of-the-art results, outperforming DP3 by up to 31% under heavy distractor clutter; the code is publicly available on GitHub.
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Likelihood-free inference with nuisance parameters through normalizing flows
Researchers decompose normalizing flows to derive near-pivotal statistics for likelihood-free inference with nuisance parameters, recovering the t-test and beating Welch limits.
A new paper decomposes neural-network normalizing flows to uncover pivotal statistics in the presence of nuisance parameters using only a sample generator from the distribution of interest. The statistic is near-pivotal in the sense of minimum average KL-divergence of its p-values and can incorporate prior knowledge of group invariances such as translation and scale. Experiments show it recovers the one-sample t-test almost exactly, outperforms the Welch test on worst-case size over a constrained variance-ratio range, and delivers higher power and much faster runtime than profile likelihood-ratio techniques on small-to-moderate samples.
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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%).
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HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning
HybridFLow uses SDN topology visibility to partition federated-learning clients into sync/async groups, reaching 80% accuracy 33-40% faster than SmartFLow.
HybridFLow is a closed-loop, SDN-driven orchestration framework for hybrid federated learning that integrates network-layer intelligence into cross-silo training. It leverages the SDN controller's global topology view to generate calibrated per-client communication-time estimates, partitioning clients into synchronous and asynchronous groups while balancing round latency and update staleness, with measured times fed back after each round. Across multiple network topologies it reaches 80% target accuracy 33-40% faster than SmartFLow and cuts average round duration by 30-40 seconds, while FedAsync fails to reach target accuracy under non-IID data.
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Introducing the CyberAgents Exchange AI Inspector: Rigorous review for community-built AI
Tenable and OpenAI launch the CyberAgents Exchange AI Inspector to security-review community-submitted AI agents, MCP servers, and skills using GPT Cyber models.
Tenable and OpenAI announced the CyberAgents Exchange AI Inspector, unveiled at OpenAI's "Intelligence at Work: Cyber Summit," to vet community-submitted AI agents, skills, MCP servers, and multi-agent playbooks in the CyberAgents Exchange registry. The process combines Tenable One AI Exposure scanning, OpenAI GPT Cyber model assessment, and human review, with reviews anchored to specific Git commits. The registry launched in August and hosts over 100 AI listings; the Inspector is expected to be available in September and has already detected prompt injection implemented via invisible Unicode tag characters in a SKILL.md file.
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US Agencies Warn China Is Systematically Extracting Frontier AI Capabilities
NSA, CISA and FBI warn Chinese AI firms including DeepSeek and Moonshot systematically extracted billions of tokens from US frontier models since late 2024.
The NSA, CISA, and FBI report that China-based AI companies including DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun, and Z.AI extracted billions of tokens from US frontier models such as Claude, GPT-4/GPT-5, Gemini, and Grok 4 since late 2024. The distillation trained DeepSeek's R1 and V3 and Moonshot's Kimi-K2/K3 models, and the agencies mapped the tactics to MITRE ATLAS while noting additional novel techniques like subscription exploitation and request metadata sanitization. They describe the activity as a strategic economic threat to US technological leadership and recommend behavioral detection, differential privacy, and targeted cost-imposing responses.
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Curriculum Learning as Transport: Understanding Curricula with Wasserstein Geodesics
Researchers model curriculum learning as Wasserstein transport over difficulty distributions, finding curriculum benefits are strongly task- and budget-dependent with no dominant strategy.
The framework represents curricula as trajectories of training distributions over discrete difficulty levels, decoupling ordering, matched exposure, endpoint smoothness, and pacing. Across a calibrated suite of 12 tasks and 33 difficulty axes under fixed training budgets, no single strategy dominates, though easy-to-hard ordering improves hard-level performance relative to exposure-matched static sampling. Endpoint smoothness and pacing substantially affect where along the difficulty spectrum a curriculum is effective, and the transport view supports extensions to learned pacing and structured difficulty spaces.
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Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training
Controlled mid-training experiments on Qwen3-8B-Base find each domain has a 10-40% coverage optimum and domain gaps survive alignment SFT.
Using Qwen3-8B-Base (with a 4B replication) across five semantically rule-disjoint KOR-Bench domains, the authors train 30 data allocations spanning the five-domain simplex at five seeds each. All five domains show interior optima in the moderate 10-40% coverage band, and domain gaps persist after a fixed-budget compensatory SFT pass, which raises 116/120 cells yet bridges 0/240 pairs at a 5% threshold. Zero coverage collapses mid-training-only accuracy, though a FineWeb-Edu-only control shows the collapse is partly generic drift. The results argue mid-training data composition requires principled design rather than reliance on later alignment.
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Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment
Researchers audit 31 NLP techniques for clinician-annotated suicide risk prediction, finding only 5 of 31 comparisons yield reliable gains.
A study of 1,635 clinician-annotated social media posts ran roughly 300 controlled experiments across 7 methodological families, auditing techniques such as model scaling, synthetic data, ensembling, and threshold tuning under severe class imbalance. The proposed system reformulates risk factor prediction as entailment between posts and codebook definitions, using architecturally diverse ensembles with class-balanced training and deployment-consistent calibration. It scores 0.8203 for risk, 0.7953 for evidence, and 0.7045 macro-F1 for factors, ranking third among 53 teams.
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When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems
Researchers propose a formal framework for governed proactive AI agency, linking activation decisions to continuing authorization, accountability, and bounded delegation in symbiotic systems.
The paper defines the activation problem: persistent AI assistants must decide whether a situation warrants behavior, and whether to act, ask, monitor, defer, or deliberately refrain. It distinguishes autonomous from delegated agency and defines symbiotic agency as delegation under a standing, revocable mandate, coupled to the principal's situation with calibrated inference and bounded personalization. The framework links activation decisions to authorized perception, behavior selection, authority containment, traceable restraint, and constrained adaptation, and provides an agency classification method, evaluation framework, benchmark scenarios, and reference architecture for always-present assistants and embodied support systems.
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Scores Alone Do Not Prove Discovery: The Discovery Certification Protocol for Auditing AI Research Agents
Researchers introduce the Discovery Certification Protocol, an auditable test framework that verifies whether AI research agents' claimed discoveries are genuine.
The Discovery Certification Protocol (DCP) converts AI research agents' discovery claims into executable recovery and feedback tests organized as gated audits. Controlled audits in SQLite optimization and virtual catalyst control produced zero recoveries in 96 episodes, with an upper bound of 0.0468. A deterministic, LLM-free verifier reproduces audit decisions from frozen evidence, giving AI research a common evidence language for outcomes, alternative routes, and feedback effects.
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Understanding the Privacy-Preserving Potential of HTTP/2 Against Webpage Fingerprinting
Researchers show HTTP/2 features can emulate website fingerprinting defenses like FRONT and Tamaraw with tunable privacy-overhead trade-offs.
An arXiv paper demonstrates that application-layer defenses such as HTTPOS, LLaMA, FRONT, ALPaCA, and Tamaraw can be emulated through HTTP/2 features at both the client and server side, including proactive resource suggestion, multiplexing, and flow control. The authors propose a unified evaluation blueprint that calibrates defense parameters per dataset, combines practical attacks with information-theoretic leakage estimators, and measures overheads to map each defense's privacy-overhead trade-offs.
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Fake Software Installers Disable Windows Update and Weaken Microsoft Defender
Fake software-download sites distribute installers that disable Windows Update and weaken Defender, attributed to China-linked cluster Silver Fox.
Microsoft says an active campaign uses counterfeit vendor websites on .com.cn and .hl.cn infrastructure with Chinese-language lures to deliver server-side generated installers that establish scheduled-task persistence, add Defender exclusions, delete shadow copies, and stop services including wuauserv, UsoSvc, uhssvc and WaaSMedicSvc. Victims span healthcare, manufacturing, gaming, technology, logistics, government and education, primarily China-based operations of multinationals and Chinese-speaking users. Microsoft assesses with moderate confidence the activity matches the Silver Fox (Yinhu) cluster, historically tied to Gh0st RAT and ValleyRAT, with C2 over non-standard ports like 5090 and 7088-7090 via domains iualef[.]net and oijfwe[.]net. Kaspersky separately detailed a QN Wallpaper DLL-sideloading chain delivering ValleyRAT.
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Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems
Paper models multi-agent LLM orchestration as a bilevel game, proving transcript-only gating limits and introducing grounded-memory SRMA.
A new paper frames orchestrator-worker coordination in multi-agent LLM systems as a bilevel coordination game and analyzes free-form reflection as stochastic movement over semantic memory states, deriving finite-time bounds and an information-theoretic impossibility result: no gate observing only the generated transcript can uniformly improve over text-indistinguishable environments, while an environment-grounded gate can. The authors propose Stochastic Reflective Memory Ascent (SRMA), which accepts candidate memory only when grounded evaluation risk strictly decreases, with geometric or polynomial convergence guarantees. On 500 SWE-bench instances, a Kimi-based instantiation of the full system resolves 72.2% versus a 70.8% public mini-SWE-agent reference.
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Abnormal AI expands email security from detection to data protection and phishing-simulation training
Abnormal AI expands its email security platform with Control Center, Email DLP Rules, and AI Phishing Coach upgrades for outbound protection and training.
Abnormal AI announced three new capabilities for its email security platform: Control Center for custom detection models and rules with decision transparency, Email DLP Rules combining regex and policy controls with an AI Triage Agent for outbound mail, and upgraded AI Phishing Coach for adaptive phishing simulations. Custom AI Models and Phishing Coach upgrades reach general availability on August 31, while Custom Rules and Email DLP Rules enter early access the same day. The launch positions Abnormal across inbound attacks, outbound data loss, and employee training.
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One in four MCP servers opens AI agent security to code execution risk
Noma Security whitepaper finds most popular AI Skills and many MCP servers carry high-risk capabilities, with state changes most prevalent.
Noma Security analyzed hundreds of popular MCP servers and Skills across eight risk categories, finding most widely used Skills carry at least one risky characteristic and a typical enterprise runs well over a hundred high-risk agent tools, with arbitrary code execution common across MCP servers. The most prevalent risk is the ability to change state or data, and named toxic combinations include ContextCrush data leakage, ForcedLeak via poisoned Salesforce CRM records, DockerDash supply-chain compromise, the Replit production database deletion, and the hijacked Amazon Q VS Code extension. Building on OWASP LLM06:2025, the paper proposes the No Excessive CAP framework of capabilities, autonomy, and permissions, recommending allowlisting, MCP version pinning, approval gates on irreversible actions, and user-scoped expiring credentials.
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[AINews] Poolside gets $12B reverse-execuhire to NVIDIA; founders stay for $1B, employees go for $6B, Infraco scaling to 7GW neocloud
NVIDIA struck a $12B deal with AI coding startup Poolside, licensing its Model Factory and hiring 109 of its technical employees.
NVIDIA spent roughly $12B in an unusual reverse-execuhire of Poolside, licensing the company's Model Factory while hiring 109 of its ~115 technical staff; founders retain a $1B stake and employees receive about $6B. Poolside had raced to raise $2B to fund a 40,000 GB300 cluster after missing a six-week funding window, and founders argue frontier-scale training now requires an order of magnitude more compute plus contracted data center space. An infrastructure arm spun out in January 2026 is scaling toward 7GW as a neocloud. The newsletter also recaps OpenAI and Anthropic agent-platform releases.
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Germany moves to give spy agencies hacking and sabotage powers
Germany's cabinet approved a 732-page bill granting BND and BfV intelligence agencies new hacking, sabotage, and disinformation powers pending parliamentary approval.
Germany's cabinet approved draft legislation that would give the foreign intelligence service BND and domestic agency BfV active operational powers, including hacking foreign systems, sabotaging supply chains with faulty components, disabling servers of hostile state-sponsored hackers, and feeding false information to extremists inside Germany. The 732-page bill, the largest overhaul of postwar German spy laws, requires telecom carriers and digital providers to assist the agencies, bars measures endangering life, and imposes new statutory controls on how agencies use AI analysis, including judge-supervised spot checks of machine-generated outputs. The reforms stem from a Federal Constitutional Court ruling on surveillance proportionality and are expected to pass parliament, with the government aiming for the law to take effect next year; civil liberties groups plan to challenge it.
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OpenAI Launches GPT-5.6-Cyber with Reduced Safeguards for Exploit Development
OpenAI released GPT-5.6-Cyber for vulnerability research and pentesting via Daybreak Red, completing 95% of advanced cyber task evaluations.
GPT-5.6-Cyber, built on GPT-5.6 Sol, targets zero-day discovery, exploit chain development and incident response with reduced refusals, scoring 95.0% on OpenAI's Advanced Cybersecurity Completion Rate versus 1.5% for GPT-5.6 Sol and 57.3% for GPT-5.5-Cyber. The model found CVE-2026-15903 (CVSS 8.8), an out-of-bounds read/write in Chrome's V8 JavaScript engine that Google patched in mid-July 2026. It is available to trusted partners including CrowdStrike, Palo Alto Networks and Cloudflare through the Daybreak Red access tier.
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