Search: “detection-as-code”

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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 News·Sep 8, 13:20 UTC · 8d agoTools1

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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 Online·Sep 8, 15:05 UTC · 7d agoTools1

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Mars Security brings threat intelligence to detection in real time

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

Mars Security announced a capability that automatically turns newly published threat intelligence from sources like CISA, Mandiant, Unit 42, and Microsoft into MITRE ATT&CK-mapped detection rules. Each rule is written in the native query language of the customer's telemetry (CrowdStrike Falcon, Wiz, Splunk, firewalls, identity providers, AWS, Snowflake, Databricks) and backtested against 30 days of the customer's data before deployment, with indicator scoring to drop noisy or stale indicators. The platform also maps existing detection coverage, flags gaps such as AWS CloudTrail tampering and pass-the-hash movement, and delivers some recommendations as open pull requests for detection-as-code workflows.

Help Net Security·Sep 8, 13:43 UTC · 8d agoTools

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GuardBreaker: Derailing AI-assisted malware analysis with a code comment

ESET names 'GuardBreaker': UAC-0099 embeds a nuclear-weapon question in VBScript comments to trip LLM scanner guardrails during analysis of its MATCHBOIL loader.

ESET researchers observed the Russia-aligned group UAC-0099 inserting a decoy prompt injection into a VBScript used to install its MATCHBOIL loader in an attack against a Ukrainian target, aiming to make LLM-based code scanners refuse and stop inspecting the file. The comment triggers safety guardrails with a request about building a nuclear weapons but has no runtime effect. Similar LLM-thwarting tricks have appeared in malicious PyPI and npm packages reported by Socket and StepSecurity. ESET recommends multi-model cross-validation of AI-assisted analysis and treating missing LLM output as requiring further checks.

ESET WeLiveSecurity·updated Sep 11, 14:32 UTC · 4d ago·first Sep 10, 09:00 UTC · 6d agoAI safety & security 3 sources1

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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.CR·Sep 15, 14:35 UTC · 23h agoResearch1

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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.

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

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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.

Cyble·Sep 4, 13:23 UTC · 12d agoIndustry

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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.CL·Sep 15, 17:20 UTC · 21h agoAI research

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Once popular for attacking AI, ASCII smuggling is embraced by spammers

Spammers adopt ASCII smuggling—invisible Unicode tag characters—to evade email filters, with Microsoft Defender detections spiking to 2.5 million per day.

ASCII smuggling hides text in Unicode tag characters (e.g., U+E0041 for "A") that are invisible to humans but readable by LLMs and text processors. The technique gained attention as a stealthy prompt-injection vector and is now used by spammers to obfuscate keywords from email detectors. Microsoft reported Defender for Office smuggling detections jumped from roughly 21,000 per day to over 1.3 million in early February, reaching 2.5 million within four days, before falling sharply in mid-May.

Ars Technica · Security·Sep 4, 17:18 UTC · 11d agoPhishing & fraud

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Operation ASTERIX: Anatomy of a Crypto Fraud Pipeline

Rapid7 exposed infrastructure behind a cryptocurrency fraud pipeline using phishing panels, voice-dialing scripts, fake wallets, and AI coding assistants.

Rapid7 researchers identified an exposed web directory on infrastructure used to support a cryptocurrency fraud operation tracked as Operation ASTERIX. The server contained raw phone-number datasets, account-validation tools, enriched lead records, phishing panels, voice-dialing scripts, fake wallet applications, persistence mechanisms, and Telegram exfiltration code. Recovered prompts, shell history, and project files show the operator relied on AI coding assistants to package Electron applications, obfuscate code, troubleshoot builds, and modify phishing infrastructure.

Rapid7 Blog·Aug 17, 11:29 UTC · Aug 17, 2026Phishing & fraud

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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.CR·Sep 8, 08:04 UTC · 8d agoResearch

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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.CL·Sep 7, 17:26 UTC · 8d agoAI research1

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The Model Proposes, the Code Disposes: A Pre-Registered Ablation of a Verifier-and-Acceptance Stage in an LLM-Orchestrated Offensive-Security Agent

Pre-registered ablation finds a model verifier stage in an LLM offensive-security agent suppresses findings; removing it eliminated suppression with precision tradeoff.

The paper evaluates a verifier-and-acceptance stage in an LLM-orchestrated offensive-security agent via a pre-registered 20-run confirmatory ablation and a 2x2 factorial study with 40 runs on vulnerable lab targets. Removing the stage eliminated pre-report suppression (median 2 vs 0 findings, p = 0.00003) but reduced model-blinded shipped precision (0.471 vs 0.353, p = 0.0087). Suppression was attributed to the model verifier rather than deterministic acceptance rules, and an instrumented canary recorded zero external contacts in all 60 runs. The full design retained 93.8% of model-adjudicated true candidates but failed its pre-registered non-inferiority floor of 0.90.

arXiv cs.CR·Sep 14, 17:13 UTC · 1d agoResearch

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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.CR·Sep 11, 09:46 UTC · 5d agoResearch

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Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection

Researchers unveil Repeat-After-Me, a black-box visual prompt injection achieving over 80% success on Qwen3.6-27B and 47% on GPT-5.5.

Researchers present Repeat-After-Me, a black-box adaptive visual prompt injection that induces frontier VLMs to reveal PII or make malicious tool calls via injected images. It exceeds 80% attack success rate on Qwen3.6-27B and 47% on GPT-5.5 even when the benign user prompt is unrelated and does not authorize the injected task. In a real-world OpenClaw Discord deployment, a minimally injected image can overwrite TOOLS.md, enabling later remote code execution and secret exfiltration.

arXiv cs.CR·Sep 3, 22:44 UTC · 12d agoAI safety & security

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ASCII smuggling crosses over from AI prompt injection to phishing evasion

Microsoft details high-volume phishing campaign using ASCII smuggling (Unicode tag chars) for filter evasion, peaking at 2.3M messages.

Microsoft researchers observed a high-volume finance-themed phishing campaign using invisible Unicode tag characters (U+E0000–U+E007F), a technique known from AI prompt injection research as ASCII smuggling, to split lure words like 'funding' and evade email filters. Telemetry from Microsoft Defender for Office 365 showed signature hits jump from roughly 21,000 messages on February 8, 2026 to more than 1.3 million on February 9, peaking above 2.3 million on February 11, with elevated weekday activity lasting approximately three months. The discovery emerged from prompt injection protection research, showing AI-era evasion techniques crossing into traditional phishing. Most messages were flagged by layered Defender protections rather than a single Unicode-specific signal.

Microsoft Security Blog·Sep 3, 16:00 UTC · 12d agoPhishing & fraud in the wild

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LLM Forensics: Where Do Backdoors Hide? Localizing and Controlling Trigger Mechanisms with Sparse Autoencoders

Researchers use sparse autoencoders to localize trigger-based backdoor mechanisms in 1B and 8B LLMs, finding detection features differ from causal control features.

In a controlled language-switching backdoor setting where fixed trigger sequences make 1B and 8B language models continue English prompts in French or German, the authors train sparse autoencoders (SAEs) across layers and transformer components. Attention and MLP features detect triggered prompts with near-perfect F1, but ablating them rarely suppresses the language switch, while residual-stream features can suppress triggered generation and some can induce target-language continuations without the trigger. The work decomposes token-trigger mechanisms into distinct SAE feature roles: trigger detection, residual-stream propagation, and language tracking, a decomposition the authors expect to transfer to other trigger-based backdoors.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 16:47 UTC · 8d agoAI safety & security1

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AWS puts AI vulnerability detection to the test, and false positives pile up

AWS publicly released its Deception Benchmark (14,822 samples) showing leading AI models falsely flag 41-99% of safe code as vulnerable.

AWS released its Deception Benchmark publicly, containing 14,822 samples across 16 programming languages and more than 70 CWE categories, with 9,695 scored samples split into 6,988 code-level and 2,707 environment-gated challenges. AWS evaluated 12 models from five providers using single-turn prompts and found none met its production bar of below 10% for both false-positive and false-negative rates. With direct prompting, models caught nearly all real vulnerabilities but incorrectly flagged 41% to 99% of safe code, with precision between 52% and 71%. Asking models to prove exploitability reduced false positives by 17 to 74 percentage points but raised false-negative rates to 7-44%, with models struggling most when external controls like Kubernetes Network Policies blocked apparent exploits.

Help Net Security·Sep 14, 04:00 UTC · 2d agoAI research

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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 News·Sep 11, 11:30 UTC · 5d agoIndustry1

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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 Security·Aug 24, 04:21 UTC · 23d agoResearch1

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SpecGuard: Inference-Time Backdoor Detection For Free

SpecGuard detects backdoored LLM behavior at inference time using speculative decoding acceptance rates, adding no extra model computation.

Researchers propose SpecGuard, an inference-time backdoor detector that repurposes draft-token acceptance rates from speculative decoding as a detection signal at zero added model-computation cost. When a trigger shifts the target model toward attacker-controlled behavior, the clean draft model's acceptance rate changes, exposing the backdoor; the paper formalizes when this signal appears and shows suppressing it weakens the backdoor. Experiments across diverse backdoor types and model families show reliable detection, including stealthy cases invisible to input-level filters. Speculative decoding is positioned as a free, always-on monitor for frequently updated deployed models.

arXiv cs.CR·Sep 10, 16:51 UTC · 5d agoAI safety & security 2 sources2

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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).

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:45 UTC · 5d agoAI research1

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New AI Attack Hides Malicious Instructions in Normal-Looking Text to Evade Safety Filters

Check Point researchers show crafted prose hides policy-violating instructions that bypass all tested LLM gatekeepers, including GPT-4o mini and Llama Guard 3.

A new prompt-crafting technique embeds malicious payloads inside grammatical, natural-looking text without Base64, invisible Unicode, or obvious encodings, defeating lightweight pre-screening gatekeepers. In testing, all four evaluated gatekeeper models—gpt-4o-mini-2024-07-18, gpt-oss-safeguard:20b, claude-3-haiku-20240307, and llama-guard3:8b—classified the crafted wrappers as safe at a 100% bypass rate across 23 obfuscated prompts. GPT-5 Thinking in high-reasoning mode recovered and acted on the hidden instruction in 17 of 18 tests (~94.4%), often spending over a minute and multiple Python executions. Researchers recommend paraphrasing untrusted input, hardening gatekeeper policies, and applying defense-in-depth controls for agentic deployments.

GBHackers·Sep 11, 05:53 UTC · 5d agoAI safety & security 2 sources

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Do AI Coding Assistants Check Before They Install? A Pre-Registered Demand-Side Audit of Trust Signals in the Research Software Supply Chain

Pre-registered audit finds AI coding assistants verified provenance signals in only 9 of 1,920 trials before installing research software packages.

The study tested whether AI coding assistants check machine-readable trust signals such as SBOMs, signed releases, and provenance attestations before installing six open-source research software projects spanning HPC and quantum computing. Three models under two operating modes produced 1,920 registered trials scored from container logs. Provenance signals were opened in only 9 of 1,920 trials (0.5%) and zero of 384 control trials, with no trial running a verification command. The authors conclude publishing signals is insufficient and verification must be built into the program running the assistant.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 16:55 UTC · 8d agoAI safety & security

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Attackers conceal phishing lures using invisible Unicode characters

Threat actors use invisible Unicode characters (ASCII smuggling) to hide phishing lures and evade email security filters.

Threat actors have adopted the ASCII smuggling technique in phishing campaigns, embedding invisible Unicode characters in emails to conceal malicious lures. The approach is designed to evade email security filters that scan for visible phishing indicators. The report gives no victim counts or named campaigns.

BleepingComputer·Sep 6, 14:23 UTC · 10d agoPhishing & fraud in the wild

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Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attribution

Researchers propose a hierarchical multi-agent system that cuts ransomware analysis cost by 44% while reaching 96.57% detection accuracy.

An arXiv paper (2609.04820) presents a Cost-Aware Hierarchical Multi-Agent System (HMAS) for adaptive ransomware detection and family attribution. Specialized agents run static analysis first, with dynamic and memory modalities invoked only when confidence is insufficient or specialists disagree; a Meta Orchestrator balances accuracy against computational cost via a cost model, and a locally deployed LLM verifies difficult cases. The system achieved 96.57% accuracy, 0.96 F1-score, and 0.99 ROC-AUC for binary detection, and 0.90 macro-F1 for multiclass family attribution. Average analysis cost dropped 43.97% versus exhaustive analysis, with 56.05% of cases resolved using static evidence alone.

arXiv cs.CR·Sep 4, 07:17 UTC · 12d agoResearch

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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.CR·Sep 11, 00:19 UTC · 5d agoResearch

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Retrofitting Code Using LLMs to Support Exceptional Behavior

EXCODER combines static/dynamic analysis with LLMs to retrofit exception-handling code, achieving 85.92% pass@1 with Qwen 2.5 Coder 32B on Java benchmarks.

The paper introduces the task of retrofitting existing code with Exception Related Code (throw statements, guarding conditions, try/catch blocks) so that given Exceptional Behavior Tests pass. EXCODER performs context engineering by integrating static and dynamic program analysis output with LLMs; it was evaluated on a benchmark built from 304 methods across 75 GitHub Java projects. Combined with Qwen 2.5 Coder 32B, EXCODER achieves pass@1, 5, and 10 rates of 85.92%, 86.18%, and 86.51%, roughly 13 percentage points over baseline, and manual inspection reveals remaining limitations.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:18 UTC · 6d agoAI research1

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How Attackers Abuse VSS, and How Huntress Detects It

Huntress details how attackers abuse Windows Volume Shadow Copies for ransomware recovery sabotage and NTDS.dit credential theft, plus detection logic.

Huntress explains that attackers abuse VSS in three ways: deleting shadow copies to inhibit recovery before ransomware detonation, creating shadow copies to extract the NTDS.dit Active Directory database for offline credential theft, and manipulating shadow copy configuration. Because backup agents and RMM tools routinely create and delete shadow copies, raw events are too noisy to alert on alone. Huntress detections instead correlate VSS activity with lateral movement and credential harvesting over a time window, such as an observed sequence of PsExec spawning SYSTEM shells on a domain controller, vssadmin create shadow, a blocked deletion attempt, and DNS reconnaissance against another host.

Huntress·Sep 14, 13:00 UTC · 2d agoResearch

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