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AI the Top Priority for New Spend as Cyber Budgets Flatline

IANS survey of 500 US CISOs finds AI is the top priority for net-new security budget even as overall cybersecurity budgets stay flat.

IANS's 2026 Security Budget Benchmark Report, based on interviews with 500 US security executives, found AI was the most popular focus for incremental budget, cited by 69% of respondents. Software now accounts for 35% of the security budget in 2026, up from 29% last year, nearly matching staff and compensation. Overall median budget growth remained flat, with 55% keeping budgets flat or making cuts, while 71% of VC-backed companies increased security budgets versus 52% of public companies. Most CISOs (69%) do not expect AI to cut headcount, and 81% anticipate it creating demand for new roles and skills.

Infosecurity Magazine · 1d agoIndustry

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 · 3d agoAI research1

What 90 days and a small budget can buy in AI agent security

Versa Field CISO details hidden costs of self-hosting open-weight models and a 90-day AI agent security plan of inventory, blast-radius reduction and testing.

In a Help Net Security interview, Prasad Tharippala, Field CISO at Versa, argues running open-weight models in-house improves control but shifts hardening, patching, access control, monitoring and incident response onto the buyer, with underestimated costs in GPU infrastructure, licensing review, EU AI Act compliance and scarce AI/ML security skills. On red-teaming AI agents, he recommends testing prompt injection, indirect injection, excessive permissions, data leakage, memory and RAG poisoning, malicious tool outputs, cross-agent trust abuse and infrastructure attack paths, mapped to OWASP agentic guidance and MITRE ATLAS. He highlights the handoff between chained agents as a major risk zone and stresses exercising human approval, shutdown and rollback controls under test conditions. For teams with 90 days and small budgets, he ranks inventory, blast radius reduction and ongoing testing as the priority order.

Help Net Security · 19d agoAI safety & security

Learning-Guided Planning in Large Dynamic Action Spaces: Budgeted Tree Search for One-to-Many Mobile Charging

LP-BTS uses graph proposal policies, learned critics, and budgeted PUCT search to plan mobile charging across dynamic action spaces up to 2,813 stops.

LP-BTS is a learning-guided planning architecture for one-to-many mobile charging, where N=250 sensors induce roughly 1,125 initial candidate charging stops. A graph proposal policy concentrates candidate support, a learned value critic evaluates leaves, and edge-budgeted PUCT compares simulated futures, letting a single frozen checkpoint cover action universes from 736 to 2,813 stops. On a sealed 30-scenario confirmatory bank it attains the highest observed survival (0.4545) and alive-AUC (0.8031), though its +0.0066 survival edge over the strongest engineered comparator is statistically unresolved.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

In most cities, nobody owns the whole network

Former Waco CIO argues cellular-connected water controllers sit outside scanned networks, and accountability plus operating-budget funding—not technology—block segmentation.

Writing as Waco, Texas's former CIO, the author describes July water-sector intrusions that CISA linked to over 100 compromised systems, typically controllers on public cellular links absent from asset lists. The FBI and EPA reported incidents at utilities in at least seven states since July 27, and a Clayton County, Georgia pump station failure triggered a boil-water advisory. He argues accountability and funding—using mechanisms like the Texas Water Development Board's new cybersecurity scoring criteria—are the binding constraints, citing Waco's 43-day segmentation of five treatment plants with operating funds.

CyberScoop · 8d agoIndustry in the wild

ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF — new model trending #3 on Hugging Face

ISTA-DASLab releases GSQ-RCO non-uniform GGUF quantizations of Qwen3.8-27B down to 2.5 bpw, with task-lossless IQ3_S matching BF16 benchmark scores.

ISTA-DASLab released GGUF quantizations of Qwen3.8-27B produced with GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization), non-uniform methods that allocate per-tensor precision via gradient-based search under a total size budget. Four checkpoints range from 2.50 bpw (8.4 GB) to 3.50 bpw (11.8 GB), plus a BF16 vision projector (mmproj) enabling multimodal use. The recommended IQ3_S build is task-lossless, matching the BF16 base exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) at roughly one fifth of the BF16 size. Optional -mtp variants add a Multi-Token Prediction head for speculative decoding in llama.cpp.

Hugging Face trending models · 19d agoModel release1

Guide to Cybersecurity Budget Planning: How Much + How To | Huntress

Huntress publishes guidance on planning cybersecurity budgets and getting more value from security spend.

Huntress released a guide aimed at helping businesses plan their cybersecurity budgets without overspending. The content is promotional guidance on prioritizing security spend rather than incident reporting. No vulnerabilities, incidents, or threat activity are described.

Huntress · Aug 12, 2026Industry

LLM-Based Penetration Testing in the Presence of Honeypots

Studies honeypot-aware budget allocation for LLM attack agents, showing detector-guided policies let agents skip deception and compromise real hosts efficiently.

The paper formalizes LLM attacker behavior against honeypots as a budgeted decision process, where agents choose to continue or skip targets when honeypot suspicion arises. A detector-guided policy lets LLM agents allocate execution budget effectively across a mixed host pool in a controlled testbed. Findings show LLM-driven attackers can reason about heterogeneous artifacts and use honeypot suspicion to guide target selection, challenging traditional deception defenses that rely on realism and obscurity against human or script-driven attackers.

arXiv cs.CR · 8d agoResearch

The 12 Best Antivirus (Endpoint Protection) Software for Business, Compared and Priced

GBHackers compares 12 business antivirus products on detection, EDR depth, pricing, and platform coverage, ranking CrowdStrike and Bitdefender joint top at 8.8.

The roundup scores 12 enterprise endpoint protection vendors across detection, EDR depth, management, pricing transparency, and platform coverage. CrowdStrike and Bitdefender tie at 8.8, with Microsoft Defender for Endpoint scoring 8.5 and noted as effectively free for Microsoft 365 E5 licensees. The piece also flags that Kaspersky cannot legally be sold in the US and that Panda and Webroot now sit under WatchGuard and OpenText respectively.

GBHackers · 8d agoIndustry 2 sources1

OpenAI is putting $1 billion behind Daybreak for defenders working without enterprise budgets

OpenAI committed $1 billion to subsidize Daybreak cyber model access, training, and support for water, grid, government, banking, nonprofit, and open-source defenders.

OpenAI committed $1 billion in credits to subsidize access to its Daybreak cyber models, along with training and technical support, for defenders of water and wastewater systems, the electric grid, state and local government, community banks, nonprofits, and open-source projects. The commitment is targeted for consumption over roughly six months, starting in the United States, and follows a smaller $1 million program offered after recent attacks on US water systems. A pilot with MS-ISAC pairs Daybreak access with guided training, and more than 35 partner products joined the Daybreak Defense Network. Thousands of defenders across 2,000 approved organizations already use the service, and OpenAI also published the architecture of its agent-first Defense Factory.

Help Net Security · 12d agoAI industry

From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production

NVIDIA detailed DSX power-management results: Lambda gained 24% token throughput at fixed power, and an AI factory auto-shed 1MW via Emerald AI's grid program.

NVIDIA says Lambda's first validation of DSX MaxLPS on HGX B200 servers ran 19 nodes within a 16-node power budget, lifting cluster token throughput 24% (roughly 4M to 5M tokens/second) and improving performance per watt by 23%. NVIDIA projects DSX MaxLPS can enable up to 40% more GPU capacity for Vera Rubin NVL72 factories within the same megawatt budget. Emerald AI's Conductor platform, running at NVIDIA's Eos factory with Silicon Valley Power, responded to over 200 utility demand signals, automatically dropping power from 4MW to 3MW without interrupting priority workloads. The first dedicated DSX Flex commercial deployment is planned at a 96-megawatt Manassas, Virginia facility.

NVIDIA Blog · 1d agoAI industry

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.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research1

Drift-Constrained Optimization: Only Direction Matters in Fine-Tuning Instruct Models

Drift-Constrained Optimization reformulates fine-tuning as update-direction selection, letting Qwen3 models improve target tasks within a behavioral drift budget.

The paper specifies a behavioral drift budget before optimization and shows that update direction is the remaining degree of freedom, reformulating fine-tuning as a direction-selection problem. In a stringent QA-only setting where instruct models must still generate multi-step reasoning at inference, a coarse layer-selective probe reverses the failure of QA-only fine-tuning. Across Qwen3-8B and Qwen3-14B, these directions substantially improve scientific reasoning and multilingual translation, matching or outperforming dedicated translation systems over 100+ languages and giving stronger initialization for reinforcement learning.

Hugging Face daily papers · 5d agoAI research

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.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research 2 sources2

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 · 6d agoAI researchHN 28↑ · 2 comments1

The 12 Best Managed XDR Services, Compared and Priced

A comparison of twelve managed XDR providers covering pricing models, telemetry breadth, and distinguishing genuine MXDR from rebranded MDR services.

The article compares twelve managed XDR providers including Bitdefender, CrowdStrike, Palo Alto Unit 42, Trend Micro, Fortinet, Secureworks Taegis, Stellar Cyber, Ontinue, and ReliaQuest, highlighting pricing models and telemetry breadth. It explains that genuine MXDR must actively monitor identity, cloud, and email telemetry rather than merely ingest it, and typically costs 30-60% more than endpoint-only MDR. It also notes Sophos completed its approximately $859 million acquisition of Secureworks in February 2025.

GBHackers · 7d agoIndustry 3 sources2

TRACE: Trajectory-robust Admission with Evidence Ordering for Efficient GUI Agents

TRACE, a training-free visual token pruning framework, cuts GUI agent inference latency and memory while keeping trajectory-wide visual evidence reusable.

TRACE is a training-free framework for trajectory-robust admission and coverage-aware evidence ordering that prunes high-resolution screenshot tokens accumulated in GUI agent trajectories. It ranks visual evidence using a query-independent layout-derived interaction prior combined with instruction relevance and feature novelty, and reserves part of the budget for native tokens distributed across the screen to repair spatial coverage. A monotone KV contraction incrementally compresses retired frames into compact session state, avoiding repeated visual encoding or pruning. Experiments across six GUI benchmarks and diverse models verify effectiveness under tight budgets, with source code to be released.

Hugging Face daily papers · 8d agoAI research

Studying Without a Syllabus: Task-Agnostic Environment Preprocessing

Paper formalizes task-agnostic environment preprocessing, where agents study unfamiliar environments under a budget to build reusable artifacts for a frozen solver.

The paper formalizes task-agnostic environment preprocessing, where a studying system explores an environment under a budget and produces artifacts like indices, scripts, or procedural guidance for a frozen solver, without task examples or evaluation feedback. The authors compare unaided and archive-equipped meta-agents against fixed synthetic-practice and corpus-processing methods across six heterogeneous benchmarks. A meta-agent variant achieves the highest Avg@3 reward on five benchmarks, while fixed corpus processing remains best on the largest corpus benchmark. Studied artifacts reduce the test-time sampling needed to reach a given score, shifting computation from repeated test-time attempts to a pre-task study phase.

Hugging Face daily papers · 8d agoAI research

What do CISOs need to rest easy about future AI risks?

IANS survey of 113 CISOs finds optimism about managing future AI security risks hinges more on organizational readiness and leadership support than current controls.

An IANS AI Security Survey of 113 CISOs fielded in April and May found 41% optimistic and 38% pessimistic about their organization's ability to manage AI security risks over the next 24 months. Six organizational signals—leadership understanding of AI risk, defined governance ownership, security team effectiveness with AI tools, CISO ownership of the AI-security budget, sustainable workloads, and staffing—separate confident CISOs from pessimistic ones. Interviewed analysts cautioned that these readiness signals measure self-assessed confidence rather than actual security, and recommended hands-on AI use by security teams plus governance of third-party models.

CSO Online · 9d agoIndustry

What vulnerability prioritization looks like when KEV, EPSS, and CVSS disagree

Cohesity field CISO Joye Purser ranks KEV over EPSS over CVSS and urges 24-72 hour patching of exploited internet-facing systems.

In a Help Net Security interview, Cohesity Global Field CISO Joye Purser lays out a vulnerability prioritization framework that puts active exploitation (KEV) first, then exploit likelihood (EPSS), then technical severity (CVSS), adjusted for asset exposure, business criticality, and compensating controls. She endorses 24-72 hour remediation targets for critical exploited internet-facing vulnerabilities and describes the organizational tradeoffs and emergency procedures needed to hit them. The interview also covers honeypot failure modes when deception systems are over-connected or over-trusted, and budget guidance recommending OT/IT segmentation, phishing-resistant MFA, and tested recovery for a 400-person manufacturer.

Help Net Security · 16d agoIndustry

AI Infra Summit: NVIDIA Vera Rubin and DSX Platform Advancements Showcase Energy Efficiencies of Optimizing Tokens Per Watt for AI Factories

At AI Infra Summit, NVIDIA showcased Vera Rubin and DSX gains up to 1.4x tokens per megawatt, plus Annapurna, d-Matrix, and Pinterest partnerships.

Ian Buck's AI Infra Summit keynote before 8,000+ attendees emphasized validated agentic tokens per megawatt as the emerging AI infrastructure metric. Announcements include Amazon's Annapurna Labs collaborating on NVHBM custom high-bandwidth memory, d-Matrix integrating NVLink Fusion with Raptor XPUs, and Pinterest using Blackwell plus Dynamo inference software for conversational visual discovery. Lambda reported 23% better performance per watt with DSX MaxLPS on Blackwell servers, running 19 nodes on a 16-node power budget. NVIDIA says DSX MaxLPS combined with Groq 3 LPX on Vera Rubin NVL72 targets up to 35X token throughput per megawatt versus GB200 NVL72 for 2-trillion-plus-parameter models.

NVIDIA Blog · 1d agoAI industry

Why don't machine learning research agents overfit?

Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.

Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.

Hacker News · security · 2d agoAI researchHN 21↑ · 3 comments

Certificate failures can cost firms over $250,000

DigiCert survey finds certificate failures cost firms over $250,000 per incident, with 47-day TLS certificates set to multiply management workloads by 2029.

DigiCert's Certificate Management Outlook reports that 34% of companies experienced outages from expired certificates and 40% from mismanagement, with nearly one in four citing incidents costing over $250,000. The CA/Browser Forum's move to 47-day certificate lifespans by 2029 will require renewals eight times more often and 40 times as many domain validations. Over half of organizations manage more than 1,000 certificates, yet only 10% have automation in place, with budget constraints and legacy systems cited as key barriers.

Help Net Security · 2d agoIndustry

When Agents Slow Down: Understanding LLM Agents' Test-Time Strategies via Elo-per-token Analysis

Elo-per-token analysis shows LLM agents' marginal gains drop below independent sampling at scale; parallel sessions beat one long session.

The paper proposes Elo-per-token analysis, using a Bradley-Terry model to measure how agent performance scales with token budget on open-ended tasks with continuous scoring. Across four agents and four benchmarks with sessions up to 100M tokens, agents initially convert tokens to Elo faster than independent sampling but eventually slow below the linear-in-log-compute reference. The authors define a scaling inflection point and show that splitting 100M tokens across parallel sessions on FrontierCS Polyomino Packing gains +264 Elo over one long session and +355 over ten short sessions. Human contestants on shared AtCoder Heuristic Contest tasks improve superlinearly, indicating headroom over current agents.

Hugging Face daily papers · 3d agoAI research3· 2 reads

Cyber Command turns to veteran of intelligence agencies for top AI role

US Cyber Command appoints Rear Adm. Ronzelle Green as chief AI officer as its AI budget grows from $5M to $138M.

US Cyber Command has named Ronzelle Green, previously head of research and development at the National Geospatial-Intelligence Agency, as its new Chief Artificial Intelligence Officer, replacing Brig. Gen. Reid Novotny. The command's 'AI for Cyber Operations' budget request grew from $5 million in fiscal 2026 to $138 million in fiscal 2027, aiming to let cyber operators process data and respond to threats faster than humans alone. Sources say Green's priority will be consolidating fragmented AI pilot programs across the organization, in contrast to the NSA's more established Artificial Intelligence Security Center created in 2023.

The Record · 6d agoAI policy 2 sources1· 1 read

Getting ahead of ‘harvest-now-decrypt-later’: Post-quantum cryptography planning

Opinion piece urges organizations to begin post-quantum cryptography migration now, citing harvest-now-decrypt-later risk and NIST deadlines.

CSO Online outlines why harvest-now-decrypt-later makes long-lived sensitive data a current risk even before quantum computers exist. It cites NIST IR 8547 timelines deprecating RSA-2048 and ECC P-256 by 2030 and removing them by 2035, finalized FIPS standards ML-KEM, ML-DSA, and SLH-DSA, upcoming FN-DSA (FIPS 206), NSA requirements for national security systems from 2027, and UK NCSC phased guidance through 2035. The author recommends cryptographic discovery, crypto-agility, and prioritizing long-confidentiality data and TLS endpoints.

CSO Online · 6d agoResearch

12 Best Patch Management Software Compared (2026): Features & Pricing

GBHackers ranks NinjaOne, ManageEngine, and Automox atop twelve patch management tools for 2026, emphasizing third-party application coverage.

GBHackers scored twelve patch management platforms on coverage, automation, visibility, deployment, and value, with NinjaOne ranked highest at 4.55. Action1 is highlighted for its genuinely usable free tier, ManageEngine for third-party catalog breadth, and Automox for cloud-native cross-OS automation. The piece notes that unpatched known vulnerabilities remain a top initial-access vector, citing CISA's Known Exploited Vulnerabilities catalog.

GBHackers · 6d agoIndustry 4 sources

Generative Late-Interaction Embeddings For Visual Document Retrieval

GLIE compresses visual document retrieval embeddings to four vectors per page while retaining nearly 80% of uncompressed nDCG@5 accuracy.

Researchers analyzing late-interaction retrieval embeddings found they lie exactly on the unit sphere and concentrate near a manifold of intrinsic dimension five to six. GLIE learns a few k vectors per page that serve as a lightweight index and a basis to regenerate the full embedding set for exact rescoring of top candidates at query time. On ViDoRe v1 with four vectors per page, GLIE retains nearly 80% of uncompressed nDCG@5 versus 70% for the best prior post-hoc method, using a 415K-parameter network trained in under three GPU-minutes on 1,000 pages.

Hugging Face daily papers · 7d agoAI research

Quantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers

Quantum IQP circuit features lift logistic-regression credit-default F1 from 0.462 to 0.517, beating Kernel PCA at an equal feature budget.

Using the UCI Default of Credit Card Clients dataset and five-fold cross-validation, an 8-qubit IQP circuit adds 16 features that raise Logistic Regression F1 from 0.462 to 0.517 (+0.055, p < 0.0001). Kernel PCA, the best classical non-linear alternative, reaches only 0.493 at the same feature count, with the gap surviving Benjamini-Hochberg correction across 12 tests (p = 0.00007). Only the linear classifier benefits, pointing to a linear-expressivity mechanism. Feature selection matters: Random Forest importance-guided selection reaches F1 = 0.523 while maximally uncorrelated features drop to 0.496.

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

Hugging Face's new ML Intern lets anyone run machine learning experiments through a simple chat

Hugging Face launched ML Intern, a chat-based agent that autonomously selects models, trains, and ships demos within user-approved compute budgets.

Hugging Face's ML Intern lets users describe a project in chat, then finds models, datasets, and tools on the Hub, GitHub, and the web before estimating compute costs and enforcing an approved budget. It autonomously creates datasets, trains models, monitors jobs, uploads results, writes reports, and builds demos, each with its own tracking dashboard. A demo training run took about six hours and cost under $0.50. The launch comes as Hugging Face is being acquired by Nvidia, whose CEO Jensen Huang has pledged to keep the platform open and hardware-neutral.

The Decoder · 7d agoAI industry1

Online Learning with LLM Experts from Limited Feedback

Paper proposes bandit algorithms for adaptively routing prompts to LLM experts, minimizing regret under limited feedback budgets.

The paper formulates adaptive prompt routing to K LLM experts as a contextual bandit problem with d prompt features over T rounds. Proposed algorithms strategically select actions and observe rewards, achieving O(dT/m) regret in the full-information setting and O(dTK/m) in the bandit setting, where m is the feedback budget. Experiments demonstrate efficient learning of high-quality routing strategies across diverse LLMs from limited feedback.

Hugging Face daily papers · 12d agoAI research