Search: “detection-engineering”

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Enterprise Defenses Recovered at the Edge and Collapsed Inside

Picus Labs' Blue Report 2026 finds perimeter prevention at 69% but post-compromise prevention just 37%, with reconnaissance blocked only 10% of the time.

Picus Labs' Blue Report 2026, based on 434,000+ simulated attacks across client production environments in H1 2026, found perimeter prevention effectiveness rose from 62% to 69% while the Post-Compromise Prevention Rate was only 37%. Quiet techniques fared worst: reconnaissance was blocked 10% of the time, registry-based credential access less than 1%, and the alert score stayed at 14% despite logging at a four-year high of 58%. IOC-based prevention fell to 50% from 71% in 2024, and Mimikatz's LSASS path was blocked about 94% while alternative credential-read paths went nearly undetected.

The Hacker NewsAug 12, 11:41 UTC · Aug 12, 2026Research

A Detection Engineer's Guide for Delegating Work to AI

Huntress argues detection engineers should only delegate security work to AI when outputs can be independently verified.

A Huntress detection engineer argues that the deciding factor for handing tasks to AI is whether the output can be checked, not whether the model is trusted. The piece frames human verification as the gate for delegating security engineering work to AI assistants. It is guidance/opinion aimed at defenders building detections with AI help.

HuntressAug 19, 04:00 UTC · 28d agoAI safety & security

338 Million Attack Simulations Reveal The State Of Enterprise Defense

Picus Labs' Blue Report 2026, from 338 million attack simulations, finds defenses strong at the perimeter but blocking only 37% of post-compromise actions.

Picus Labs' fourth annual Blue Report analyzed over 338 million attack simulations from production environments in H1 2026. Average prevention effectiveness rose from 62% to 69%, but only 37% of attacker actions were blocked after compromise, with reconnaissance and credential theft largely missed. IOC-based malware download prevention fell to 50% from 71% in 2024, and Mimikatz credential dumping from LSASS memory was blocked 94% of the time versus 17% from other memory locations and 3% from registry.

Help Net SecurityAug 12, 00:00 UTC · Aug 12, 2026Research

Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection

Det-LIME extends LIME to multi-instance object detection explanations, improving attribution for harbor seal aerial surveys.

Det-LIME adapts LIME to object detection by combining per-detection weighting, a proximity kernel emphasizing box-adjacent regions, and IoU-based matching to track instances across perturbations. It was evaluated on aerial drone imagery for harbor seal detection plus a seabird case study, and compared against vanilla LIME, Stabilized LIME, Deterministic LIME, and gradient-based attribution. Using Attribution Ratio and Max Saliency Hit Rate metrics, it consistently improved multi-instance attribution and produced box-aligned explanations useful for debugging and data augmentation.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:20 UTC · 1d agoAI research

Windows Malware Detector as a Compound AI System: Trade-Offs in Accuracy, Efficiency, and Adversarial Robustness

Researchers model industrial Windows malware detection as a Compound AI System, quantifying trade-offs between detection accuracy, efficiency, and adversarial robustness under tiered attacker knowledge.

Industrial Windows malware detectors combine rule-based mechanisms with ML-based static and dynamic analyses, but their architectures are rarely publicly disclosed. The authors propose a methodology balancing detection performance, computational cost, and robustness, plus system-level threat models capturing whole-pipeline evasion rather than isolated components. Experiments on real-world data show training time reductions with marginal detection loss, while more knowledgeable attackers craft increasingly effective adversarial examples. The paper derives deployment guidelines from the observed efficiency-robustness trade-off.

arXiv cs.CRSep 8, 08:04 UTC · 8d agoResearch

Poster: Towards Selecting Threat Appropriate Industrial Intrusion Detection Systems

Poster proposes counter-threat-intelligence-based detector selection for industrial control systems, showing IDS performance varies strongly by attack scenario.

The poster proposes a counter-threat intelligence sharing mechanism to select appropriate intrusion detection systems for the current threat situation in industrial control system environments. Attack-level performance evaluations of various IDSs show detection performance varies depending on the attack scenario. The results emphasize the benefit of dynamically matching detectors to evolving ICS threats.

arXiv cs.CRSep 11, 09:46 UTC · 5d agoResearch

Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks

ExCYDER framework self-verifies anomaly detection alerts for DER power grids using LightGBM and SHAP, reaching over 98% detection accuracy.

The paper presents ExCYDER, an explainable AI anomaly detection framework for Distributed Energy Resource networks that combines LightGBM with SHAP to validate whether each model decision aligns with its feature-attribution evidence. On a realistic DNP3 dataset it achieved over 98% detection accuracy, 14.5 ms SHAP latency per alert, and confidence deviation within 5%. The self-verifying mechanism distinguishes coherent from inconsistent alerts, improving interpretability and auditability for DER-focused security operations centers.

arXiv cs.CRSep 11, 00:19 UTC · 5d agoResearch

AI-Driven Threat Intelligence for Gulf Enterprises: Why Detection Speed Is Now a Regulatory Requirement

Cyble argues GCC regulators' 6-72 hour breach-notification deadlines make AI-powered detection essential for Gulf enterprises.

Cyble's blog highlights that the UAE Information Assurance Standard v2 requires incident notification within 6 hours of detection, while Saudi Arabia's SAMA cybersecurity framework and NCA Essential Cybersecurity Controls converge on 72-hour reporting. It argues that compliance clocks start at detection, not response, and that IAS v2 mandates 24/7 monitoring with defined SLAs for Tier 1 critical infrastructure entities. The piece promotes Cyble Vision's AI-powered threat intelligence for continuous exposure monitoring and audit-ready detection logs.

CybleSep 4, 13:23 UTC · 12d agoIndustry

FreqSpaNet: Frequency and Spatial Learning of SFPF for Physical Layer Hardware Integrity Detection

FreqSpaNet learns spatio-frequency polarization fingerprints to detect unauthorized wireless hardware replacement, reaching 96.31% mean AUROC across seven replacement scenarios.

FreqSpaNet is a representation learning network for open-set hardware anomaly detection using spatio-frequency polarization fingerprints (SFPFs), which capture device-dependent responses across frequencies and directions. A frequency branch models local variations among neighboring frequencies while a geometry-aware spatial branch models directional relationships via angular information, combined through adaptive fusion and complementary pretraining. It achieves a mean AUROC of 96.31%, 9.05 points above the baseline, and is verified under seven hardware replacement scenarios.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:32 UTC · 1d agoAI research1

The History Is the Detector: Executing CVE Patch History, End-to-End

BUGSTONE-E2E converts CVE patch history into executable LLM-guided detection rules, yielding 1,033 rules and 644 runtime-verified findings across 14 programs.

The BUGSTONE-E2E framework mines reusable detection rules from verified fixing commits, organized by CWE and language, and applies them through a funnel pipeline that escalates from Tree-sitter anchors and lightweight heuristics to LLM-based agent inspection, runtime verification, and scope-checked patch generation. Built from 19,325 high-severity CVEs published 2022-2026, it produced 1,033 detection rules spanning 56 CWE families, packaged into 172 skills. Applied across 14 programs, it generated runtime evidence for 644 findings, demonstrating that vulnerability history can drive reproducible detection and repair.

arXiv cs.CRSep 4, 16:40 UTC · 12d agoResearch

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.RUNAug 12, 07:58 UTC · Aug 12, 2026Industry

Feature Recovery for Object Understanding After Irreversible Fire Damage

TRACE benchmark with 21.4K scenes studies post-fire object understanding; a Feature Recovery Module improves degraded-image retrieval by 12.5% and material recovery by 20.1%.

The paper introduces TRACE, a transformation-aware benchmark with 21.4K real-image-grounded synthetic scenes, 499 object identities across 189 categories, and five tasks covering degraded-object detection, pristine-state recovery, material recovery, description generation, and functional reasoning. Existing models degrade sharply: RF-DETR mAP falls 71% relative from least to most severe level, and InternVL3.5 retrieval R@1 drops from 93.85 to 28.11. The proposed Feature Recovery Module maps degraded encoder features to pristine-aligned representations while keeping the host model frozen, averaging relative gains of 12.5% for retrieval and 20.1% for material recovery across VLM hosts and severity levels.

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

The Vulnerability Gap: Why Discovery Is Outrunning Repair

Dark Reading argues AI-accelerated vulnerability discovery and tightening regulation are widening the gap between flaw discovery and repair capacity.

The article argues that AI tooling is increasing the pace at which vulnerabilities are discovered while remediation capacity has not kept up, creating a growing backlog. It frames this widening 'vulnerability gap', combined with a tightening regulatory environment, as an all-hands-on-deck moment for security teams. The piece is analysis and opinion rather than disclosure of a specific flaw.

Dark ReadingAug 24, 14:00 UTC · 23d agoIndustry

Mars Security Debuts Automated Threat Engine Processing Live Cyber Intelligence Into Validated Rules Within Minutes

Mars Security launches Real-Time Intel-Based Detection, converting threat intelligence advisories into validated, ATT&CK-mapped detection rules within minutes for SOCs.

Mars Security, an autonomous threat hunting and detection engineering platform founded by former offensive security operators, announced Real-Time Intel-Based Detection. The capability ingests advisories from sources like CISA, Mandiant, Unit 42, and Microsoft Threat Intelligence, maps indicators to MITRE ATT&CK, and authors native query logic across connected infrastructure including CrowdStrike Falcon, Wiz, Splunk, Sysmon, identity providers, Snowflake, and Databricks. Every rule is backtested against 30 days of historical telemetry to quantify false positives before analyst approval and one-click deployment. The feature is free for existing customers and available via AWS Marketplace.

CSO OnlineSep 8, 15:05 UTC · 8d agoTools2

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

ReactVAU is a slow-fast streaming framework for real-time video anomaly understanding that reserves heavyweight MLLM reasoning for suspicious events, improving efficiency.

ReactVAU addresses causal streaming video anomaly understanding with three components: a lightweight Fast Detection Module using Spatial Grid Folding, Anomaly-Aware Persistent Memory that protects critical visual cues from temporal decay, and a Slow Reasoning Module activated only on suspicious events. This design minimizes heavyweight MLLM invocations during long normal intervals. Experiments show competitive anomaly detection and causal reasoning under strict streaming constraints with significantly enhanced computational efficiency.

Hugging Face daily papersSep 6, 20:00 UTC · 10d agoAI research

SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Code

SEMA-GUARD uses semantic analysis and graph neural networks to detect vulnerabilities in assembly code, achieving 85.1% accuracy on a Juliet-derived benchmark.

SEMA-GUARD is a framework that detects vulnerabilities in compiled programs when source code is unavailable, targeting malware, firmware, and embedded systems analysis. It enriches control flow graphs with low-level execution semantics including stack manipulations, memory accesses, and data flow. Evaluated on a Juliet Test Suite set compiled to assembly and split into function-level chunks, it achieves 85.1% accuracy and an F1 score of 0.801, outperforming purely statistical or structural approaches.

arXiv cs.CRSep 15, 14:35 UTC · 1d agoResearch1

Project noRecognition: Teaching AI to Fool Surveillance Cameras

Security researcher Bill Swearingen's noRecognition project uses 31 million tested patterns to defeat license plate reader and surveillance camera AI detection.

Kansas City researcher Bill Swearingen built noRecognition, using reinforcement learning across roughly 31 million tests to generate printed patterns that break the detection layer of license plate readers and surveillance cameras. The strongest validated result achieved 61.7% non-detection against a detector taken from a real deployed camera, though most headline figures remain digital simulations. At DEF CON he covered a 2009 Toyota Yaris in a new pattern and reported it effective against a Flock Safety camera, with curved wheels the main weak point. He is crowdfunding apparel products and withholding his best patterns to prevent camera makers from blocking them.

Security AffairsAug 18, 17:05 UTC · 29d agoAI safety & security

Few-Shot Learning for Network Intrusion Detection: Methods, Datasets, and Performance

A PRISMA-style review of 21 few-shot learning studies for network intrusion detection finds meta-learning and CNNs dominant and evaluation inconsistently reported.

The systematic review screened 1,358 records from ACM Digital Library, IEEE Xplore, and Scopus covering 2022-2026 and retained 21 studies on few-shot learning for network intrusion detection. Meta-learning (8 studies) and convolutional neural networks (10) are the most common approaches, while CIC-IDS2017 and CSE-CIC-IDS2018 are the most frequently used datasets. Most evaluations use five or fewer samples per class, and missing parameters and source code limit reproducibility and direct comparison.

arXiv cs.CRSep 10, 09:07 UTC · 6d agoResearch1

Identifying Agentic Automation with Behavioral Telemetry

Akamai describes detecting autonomous AI browser agents like Comet using Masked Autoencoder Transformer models on sparse behavioral telemetry.

Akamai researchers present a behavioral telemetry approach for identifying agentic automation in web traffic. Masked Autoencoder Transformer models are used to detect the sparse behavioral signals produced by autonomous AI browser agents such as Comet. The work targets traffic classification and bot detection rather than a specific vulnerability, and becomes more relevant as agentic browsing adoption grows.

Akamai BlogAug 19, 19:00 UTC · 28d agoAI safety & security1

What researchers learned about building an LLM security workflow

Oslo and FFI researchers show structured agentic workflows lift LLM alert-triage accuracy from 0% to about 93% on malicious cases.

Researchers at the University of Oslo and the Norwegian Defence Research Establishment tested GPT-5-mini, Claude 3 Haiku, Qwen3:30B, and Gemma 3:27B on alerts from the AIT Log Data Set V1.1; given only alert descriptions and log summaries, all four models correctly flagged zero percent of true-positive cases involving reconnaissance, brute-force logins, and initial access. Wrapping the same models in a workflow with constrained SQL queries over Suricata logs, an evidence summarizer, and a verdict stage with revision loops raised malicious-case accuracy to an average of 93 percent, with GPT-5-mini identifying every malicious case across 100 runs. The authors flag it as a proof-of-concept on one synthetic scenario and note models skewed conservative on benign alerts, with GPT-5-mini marking every benign case uncertain.

Help Net SecurityAug 24, 04:21 UTC · 23d agoAI research1

BenchShield: Formal Model-Backed Instrumentation for Reward Integrity in LLM-Agent Evaluation Infrastructure

BenchShield uses lifecycle-model-backed instrumentation to detect reward hacking in LLM-agent benchmarks, lifting full-chain recall to 77-100% at up to 65% lower cost.

The framework grounds reward-hacking detection in a finite lifecycle model of an evaluation's reward-relevant events, combining a static phase-aware taint analysis with runtime infrastructure-side evidence attribution. Evaluation used a human-labeled corpus of 456 adjudicated trajectories drawn from more than 31,000 public agent runs across three benchmarks. BenchShield improves full-chain recall from 23-94% to 77-100% and same-vector coverage from 16-56% to 43-78%, cuts per-task cost by up to 65%, and achieves 96% accuracy detecting reward hacking at runtime.

arXiv cs.CRSep 10, 03:10 UTC · 6d agoAI safety & security1

An Empirical Analysis of ReDoS Vulnerabilities and ReDoS Detection Tools

Study of NVD data finds ReDoS vulnerabilities growing more prevalent and more likely to be exploited, while five detection tools disagree substantially.

The study compares five publicly available ReDoS detection tools and one regex correction tool across three datasets. An empirical analysis of all ReDoS vulnerabilities reported to the NVD finds they are becoming more prevalent and are much more likely to be exploited than non-ReDoS weaknesses. The detection tools exhibited substantial disagreement on whether a given regex is vulnerable.

arXiv cs.CRSep 9, 15:09 UTC · 7d agoResearch

Your Critical Vulnerabilities Might Not Be Your Biggest Risk

Autonomous penetration testing advocates prioritize exploitable attack paths over raw vulnerability severity for continuous security validation.

The article argues that scanner severity scores lack context: a critical flaw behind strong segmentation may be low priority, while a medium flaw on internet-facing systems can provide a foothold chained toward sensitive data. It positions autonomous penetration testing and attack path validation as the execution layer for continuous security validation, replacing point-in-time assessments. The piece is vendor-authored thought leadership rather than incident or vulnerability news.

The Hacker NewsSep 11, 11:30 UTC · 5d agoIndustry1

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.

arXiv cs.CRSep 10, 22:38 UTC · 6d agoResearch1

AI Is Ending the Era of Hidden Vulnerabilities — Are Vendors Ready?

Dark Reading argues AI-assisted bug discovery is flooding vendors with vulnerability reports, straining disclosure processes and secure-by-design commitments.

The Dark Reading analysis describes a surge of bug reports driven by AI-powered discovery, exposing bottlenecks in vendor triage and disclosure pipelines. It argues this volume is revealing secure-by-design failures and questions whether vendors can keep pace with the rising tide of findings.

Dark ReadingSep 4, 13:00 UTC · 12d agoIndustry

Can your coding style predict whether your code is vulnerable?

University of Massachusetts Dartmouth researchers present VulStyle, a stylometry-based vulnerability detector that also exposes benchmark reliability problems.

VulStyle combines stylometric features with syntax-tree structure and source tokens, pre-trained on about 4.9 million functions across seven programming languages and fine-tuned on five vulnerability detection datasets. It beat token-only detectors on some benchmarks but its F1 drops sharply on DiverseVul, which the authors link to noisy labels inflating reported performance across popular datasets. The authors argue style-aware detection should be harder to evade but did not test this empirically, and they note that uniform LLM-generated code may strip away the individual developer style the model depends on.

Help Net SecurityAug 24, 04:21 UTC · 23d agoResearch1

Online Change-point Detection for Cooperative Multi-Agent Reinforcement Learning

Researchers propose Patterns of Past Rewards (PPR), a lightweight reward-based detector that flags environment shifts in cooperative multi-agent reinforcement learning training.

The paper introduces Patterns of Past Rewards (PPR), an algorithm-agnostic detector that smooths cooperative agents' return streams and applies statistical drift testing to flag environment or task changes. Evaluation in a custom Speaker-Listener environment built on the Multi-Agent Particle Environment under two non-stationarity scenarios shows PPR balances detection speed against alarm stability. It avoids the repeated alarms of a smoothed-return baseline and the missed shifts of raw-return detection, enabling MARL systems to reliably identify major changes during training.

arXiv cs.AI / cs.LG / cs.CLSep 4, 15:49 UTC · 12d agoAI research1

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

CodeTD detects hallucinations in code LLMs before execution by analyzing topological patterns of attention maps, outperforming recent baselines.

CodeTD applies topological data analysis (TDA) to code LLM attention maps to quantify prompt-generation mismatch as a pre-execution correctness signal. Experiments cover HumanEval, MBPP, BigCodeBench, and MultiPL-E across 5 programming languages and 10 code LLMs up to 34B parameters. The method outperforms recent baselines and transfers between coding benchmarks, helping catch code that fails the task or embeds security vulnerabilities.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:26 UTC · 9d agoAI research1

Mars Security Launches Real-Time Intel-to-Detection Engine That Turns Live Threat Intelligence Into Backtested Detections in Minutes

Mars Security launched Real-Time Intel-Based Detection, converting advisories into MITRE ATT&CK-mapped, backtested detection rules for CrowdStrike, Wiz, and Splunk within minutes.

The capability turns newly published threat intelligence from CISA, Mandiant, Unit 42, and Microsoft Threat Intelligence into validated detection rules within minutes, each backtested against 30 days of the customer's own telemetry before deployment. Rules are written in native query languages across CrowdStrike Falcon, Wiz, Splunk, firewalls, Linux Sysmon, identity providers, AWS telemetry, and data lakes such as Snowflake and Databricks, with no data ingestion or stack changes. The feature is available at no additional cost to existing customers and on AWS Marketplace. Mars also flags detection coverage gaps and extends monitoring to credentials leaked by AI coding agents.

Cyber Security NewsSep 8, 13:20 UTC · 8d agoTools1

HazardAuditor: From Executable Threats to Safer Computer-Use Agents

HazardAuditor trains execution-grounded guard models for computer-use agents, improving safety verdict accuracy by up to 16.5 points.

HazardAuditor runs heterogeneous agents (Claude Code, Codex, Hermes, OpenClaw) in controlled environments and normalizes their interactions into a canonical event representation for cross-framework supervision. It introduces Guard Policy Optimization (GuardPO), which converts deterministic safety outcomes into sequence-level advantages and normalizes rationale and verdict regions so the safety decision becomes the effective optimization unit. Across multiple benchmarks and heterogeneous computer-use systems, HazardAuditor improves accuracy by up to 16.5 percentage points over the strongest prior guard model. Code, models, and evaluation artifacts are being released.

Hugging Face daily papersSep 13, 20:00 UTC · 3d agoAI safety & security

UltraViolet Cyber Equinox measures detection coverage against MITRE frameworks

UltraViolet Cyber launches Equinox, an AI-automated platform mapping SIEM and EDR detection coverage against MITRE ATT&CK and ATLAS frameworks.

UltraViolet Cyber announced Equinox, a detection engineering platform from its TIDE team that maps existing detections and log sources against MITRE ATT&CK and MITRE ATLAS. It performs coverage analysis in under 30 minutes and recommends vendor or custom detections to fill gaps, with engineers reviewing and backtesting each recommendation. In one customer trial, mapped technique coverage rose from 59 of 222 techniques (26.6%) to 136 of 222 (61.3%) without increasing alert volume.

Help Net SecuritySep 15, 13:33 UTC · 1d agoTools2

Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection

RECAL improves provenance-based APT detection with relation-balanced masked graph learning and calibrated errors, reaching 99.99% F1 on DARPA E3 datasets.

RECAL is an unsupervised framework for provenance-based intrusion detection that uses relation-balanced masked graph learning to capture rare interaction patterns, addressing statistical heterogeneity where relation frequencies differ by roughly 140,000X in CADETS. It calibrates reconstruction errors against each relation's benign error distribution to produce comparable anomaly evidence and reduce false alarms. On three DARPA E3 datasets, RECAL achieves F1 scores of 99.99%, 99.93%, and 99.99%, outperforming the best baseline on each dataset, and reduces mean false positive rate by approximately 105X, 4X, and 41X versus the lowest-FPR baseline.

arXiv cs.CRSep 15, 00:31 UTC · 1d agoResearch