Search: “nids”

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DomainAll Cyber AIWindow24h 3d 7d 30d AllIn the wild Important only Show duplicates

CategoryAll Vulnerability 6 Ransomware 4 Research 3 Policy & legal 3 Threat actor 2 Malware 2 Industry 1 AI safety & security 1 Data breach 1 AI research 1 Exploit / PoC 1

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ZDI-26-630: NI LabVIEW VI File Parsing Integer Overflow Information Disclosure Vulnerability

ZDI disclosed CVE-2026-18445, an integer overflow in NI LabVIEW VI file parsing that can disclose sensitive information, rated CVSS 3.3.

The Zero Day Initiative published advisory ZDI-26-630 describing an integer overflow vulnerability in NI LabVIEW's parsing of VI files. Exploitation can disclose sensitive information and requires user interaction, such as visiting a malicious page or opening a malicious file. ZDI assigned the flaw a CVSS rating of 3.3.

ZDI Published AdvisoriesSep 9, 05:00 UTC · 7d agoAdvisory 2 sources CVE-2026-18445

A Feature-Rich Embedded NIDS with eBPF/XDP: Detector and Architecture Trade-offs

eBPF/XDP-based NIDS with Isolation Forest reaches 0.965 live F1 on DDoS replay; gRPC microservices match monolithic accuracy within 2ms overhead.

The paper presents a DDoS-focused network intrusion detection system for transport networks built with Ericsson, combining a statistical baseline with an Isolation Forest trained on flow features from GoFlowMeter, an open-source Go implementation of CICFlowMeter, plus eBPF/XDP kernel-level traffic filtering. On a Raspberry Pi 5 testbed replaying CIC-DDoS2019 as real traffic, the Isolation Forest achieves 0.965 recall/F1 live in the monolithic variant, catching low-volume attack windows the baseline misses. gRPC microservices nearly match monolithic accuracy adding under 2ms per window, while the Kafka pipeline trails by roughly nine percentage points and adds about 27ms.

arXiv cs.CRSep 11, 09:00 UTC · 5d agoResearch

ZDI-26-697: Linux Kernel NTFS3 Out-Of-Bounds Read Information Disclosure Vulnerability

ZDI-26-697: Linux Kernel NTFS3 out-of-bounds read rated CVSS 7.3 lets local low-privileged attackers disclose sensitive information.

ZDI advisory ZDI-26-697 describes an out-of-bounds read in the Linux Kernel NTFS3 driver rated CVSS 7.3. An attacker must first be able to execute low-privileged code on the target system to exploit the flaw. Successful exploitation leads to sensitive information disclosure. No CVE identifier is listed in the advisory.

ZDI Published AdvisoriesSep 14, 05:00 UTC · 2d agoVulnerability 3 sources

ZDI-26-649: (Pwn2Own) OpenAI Codex Improper Neutralization of Control Sequences Remote Code Execution Vulnerability

ZDI published advisory ZDI-26-649 for a CVSS 7.8 remote code execution flaw (CVE-2026-19591) in OpenAI Codex, demonstrated at Pwn2Own.

The Zero Day Initiative published advisory ZDI-26-649 describing a remote code execution vulnerability in OpenAI Codex, tracked as CVE-2026-19591 with a CVSS 7.8 score. The flaw involves improper neutralization of control sequences. Exploitation requires user interaction, as the target must open a malicious folder. The bug was demonstrated at Pwn2Own and disclosed through ZDI.

ZDI Published AdvisoriesSep 10, 05:00 UTC · 6d agoVulnerability 4 sources CVE-2026-195911

NIST Seeks Public Input on AI-Ready NVD Modernization

NIST is seeking public comment on modernizing the National Vulnerability Database to support AI-powered vulnerability research.

The US National Institute of Standards and Technology announced it is soliciting public input on modernizing the National Vulnerability Database. The initiative aims to make the NVD AI-ready to support AI-powered vulnerability research and analysis.

Infosecurity MagazineAug 12, 12:30 UTC · Aug 12, 2026Policy & legal

Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS

Researchers propose DriftXpert, a concept-drift-adaptive network intrusion detection system validated on enterprise networks, addressing degraded AI-based NIDS performance in dynamic traffic.

AI-based network intrusion detection systems assume static data distributions and degrade under concept drift, raising false positives in dynamic environments. DriftXpert uses a two-stage offline framework: an unsupervised latent-manifold anomaly metric to detect traffic drift, and representation consistency alignment with cross-epoch neuron weight aggregation and selective freezing to transfer knowledge without catastrophic forgetting. Experiments on public datasets and a real enterprise network show effective adaptation to drifted data while retaining known-attack detection.

arXiv cs.CRSep 8, 11:58 UTC · 8d agoResearch

Retrospectively Reverse-Engineering Apple's Neural Engine

A developer reverse-engineers Apple's M1 Neural Engine architecture, mapping compute cores, MAC datapaths, and schedulers to explain the NPU's decline as transformers displaced CNN workloads.

A developer who previously maintained a reverse-engineered Linux driver for Apple's Neural Engine (ANE) published a retrospective deep dive mapping the M1 ANE's full internal architecture: compute, datapath, scheduler, memory, and execution model. The M1 ANE has 16 compute cores with 128 FP16 (or 256 INT8) MAC lanes each, totaling 2048 parallel MAC lanes, using 32-bit Q16.16 fixed-point accumulation with FP16 readout and an accumulator that saturates at 2^15. The author argues the ANE's dataflow was architected around the predictable reuse patterns of 2017-era CNN workloads (dating to the A11 Bionic), which autoregressive transformer decode broke, limiting its usefulness for general ML. With Apple's M5 folding ANE cores into GPU cores to tout LLM performance, the post frames this as the beginning of the end for the standalone NPU.

Hacker News · AISep 12, 07:54 UTC · 4d agoAI research HN 54↑ · 7 comments2

NIST wants to overhaul its vulnerability database for the AI age

NIST issued a Federal Register RFI seeking public input on overhauling the National Vulnerability Database for AI-scale, machine-consumable security data.

NIST published a request for information arguing the National Vulnerability Database must adapt as LLMs increasingly find and exploit vulnerabilities at machine scale. The RFI seeks input on integrating automation into vulnerability reporting, faster dissemination to defenders, and transparency and auditability in AI-driven decisions. It follows the White House-backed Gold Eagle clearinghouse at Treasury and the VINCE program with Carnegie Mellon's Software Engineering Institute for AI-discovered vulnerability reports.

CyberScoopAug 11, 15:36 UTC · Aug 11, 2026Policy & legal

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

NIS2 Compliance in the AI Age: Why Traditional Cybersecurity Isn’t Enough

Akamai argues NIS2 compliance is harder in the AI age, outlining four key challenges and urging network segmentation as essential.

An Akamai blog post claims achieving compliance with the EU's NIS2 directive is more challenging in the AI era and describes four key challenges organizations face. It argues traditional cybersecurity approaches are insufficient and positions network segmentation as essential for compliance. The piece is vendor commentary rather than new regulatory guidance or enforcement news.

Akamai BlogSep 9, 13:00 UTC · 7d agoIndustry

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.

arXiv cs.CRSep 14, 04:28 UTC · 2d agoAI safety & security

NIS2 compliance: Fixing IAM and access control before the 2026 audit

EU NIS2 enforcement deadlines approach; organizations are urged to prioritize service account inventory, lifecycle offboarding, and phishing-resistant MFA before audits.

EU member states are moving from NIS2 transposition into enforcement, with fines up to 10 million euros or 2% of global turnover for essential entities and personal liability for management bodies. The article argues access management is the fastest high-ROI starting point, estimating 2-4 weeks to enforce fine-grained password policy, vault shared credentials, and deploy phishing-resistant MFA versus 6-12 months for supply chain risk management. It flags three common pre-audit failures: unmanaged service accounts and API keys, dormant accounts from broken offboarding, and SMS OTP instead of phishing-resistant MFA under NIST SP 800-63B. The piece promotes Passwork as a single control plane for credential storage, RBAC, and WebAuthn.

Help Net SecuritySep 1, 00:00 UTC · 15d agoIndustry

OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

OmniMed-FL benchmarks multimodal federated learning for chest radiograph diagnosis across 3-20 clients, with FedProx leading under severe non-IID skew.

OmniMed-FL studies multimodal federated learning combining chest radiographs and clinical notes for five-class condition classification under HIPAA/GDDR-compliant decentralized training. It benchmarks eight fusion strategies, imputation rules, and federated baselines under Dirichlet non-IID partitioning across 3-20 hospital clients. With 5 clients and severe skew (alpha=0.1), FedProx scored 0.737 macro-F1 versus 0.662 for FedAvg and 0.297 for local-only training. Multimodal fusion beat unimodal inputs (0.956 vs 0.934 text, 0.664 images) on the synthetic corpus.

arXiv cs.AI / cs.LG / cs.CLSep 9, 15:56 UTC · 6d agoAI research1

NIST and CISA finalize playbook to stop token theft and forgery

NIST and CISA finalized NIST IR 8587, a playbook helping federal agencies and cloud providers defend identity tokens against theft and forgery.

The finalized NIST IR 8587 guidance covers protecting token signing keys, verifying tokens, lifetimes, revocation, session management, and dividing security responsibilities between cloud providers and customers. It cites an incident in which foreign actors forged tokens with a stolen commercial signing key to steal more than 60,000 emails from one government agency. It also recommends extending token protections to AI agents and preparing identity systems for a future post-quantum cryptography transition.

Help Net Security07:40 UTC · 7h agoAdvisory 2 sources

CISA and NIST Release Technical Checklist for Safeguarding Identity Tokens From Theft and Misuse

CISA and NIST published NIST IR 8587, final guidance for protecting identity tokens from forgery, theft, replay, and signing-key compromise.

NIST Interagency Report 8587 (September 15, 2026) expands the IA-13 'Identity Providers and Authorization Servers' control from NIST SP 800-53 R5.1.1, guiding federal agencies and cloud providers on SSO, identity federation, and machine-to-machine authentication. It requires hardware-backed signing-key storage for moderate-impact systems, 90-day key rotation for high-impact systems, token lifetimes under one hour, and sender-constrained mechanisms such as mutual TLS and DPoP. The report cites incidents including forged SAML assertions that exposed over 60,000 emails from a federal agency. It also extends guidance to agentic AI systems using signed tokens and urges post-quantum cryptography migration planning.

Cyber Security NewsSep 15, 17:04 UTC · 22h agoAdvisory1

NIST Warns of Unique Security Risks in Multi-Cloud Environments

NIST catalogued 23 novel security challenges unique to multi-cloud environments and urged the community to develop solutions.

NIST published an analysis identifying 23 distinct security challenges that arise specifically in multi-cloud environments. The agency explicitly encouraged the cybersecurity community to work on solutions for these gaps. The publication reflects growing official concern that spanning multiple cloud providers creates risk patterns not covered by single-cloud security models.

Infosecurity MagazineAug 24, 15:30 UTC · 22d agoAdvisory

Protecting Tokens and Assertions from Forgery, Theft, and Misuse: Implementation Recommendations for Agencies and Cloud Service Providers

NIST and CISA publish final interagency report with implementation guidance for protecting tokens and assertions from forgery and misuse.

CISA released a final NIST/CISA interagency report guiding federal agencies and cloud service providers on protecting identity assertions, access tokens, and cryptographic mechanisms underlying modern authentication and authorization. It addresses forgery, theft, and misuse of signed tokens that adversaries use for lateral movement and data access in hybrid and multi-cloud, SSO, federation, and API-based environments. The final version updates token validation, secrets management, and detection-at-scale guidance gathered via the Joint Cyber Defense Collaborative, and supports Executive Order 14306 and Secure by Design principles.

CISA AdvisoriesSep 15, 12:00 UTC · 1d agoAdvisory

Water sector example added to the NCSC’s Secure connectivity principles

NCSC UK adds a water sector example to its Secure Connectivity Principles, the first ICS community-authored content on its site.

The UK NCSC has added a water sector example to its Secure Connectivity Principles guidance. It is the first content authored by the Industrial Control System Community of Interest to appear on ncsc.gov.uk. The guidance helps ICS operators apply secure connectivity practices.

NCSC UKAug 11, 12:00 UTC · Aug 11, 2026Advisory

Ncsc Raises Alarms Prompt

The UK NCSC raised alarms about prompt injection risks in LLM-integrated systems, urging organizations deploying AI to review exposure.

The UK National Cyber Security Centre (NCSC) has raised alarms about prompt injection attacks against systems using large language models. The warning highlights how attackers can manipulate model instructions to bypass safeguards, exfiltrate data, or trigger unintended agent actions. Organizations deploying LLM-based features are advised to assess and mitigate their exposure to this technique.

Infosecurity MagazineAug 19, 00:00 UTC · 28d agoAI safety & security

ZDI-26-575: Linux Kernel Net Scheduler Packet Classifier API Time-Of-Check Time-Of-Use Local Privilege Escalation Vulnerability

ZDI publishes ZDI-26-575, a CVSS 7.5 TOCTOU local privilege escalation in the Linux kernel net scheduler packet classifier API.

The Zero Day Initiative disclosed a time-of-check time-of-use flaw in the Linux kernel's net scheduler packet classifier API that permits local privilege escalation. Exploitation requires the attacker to first execute high-privileged code on the target system. ZDI assigned a CVSS rating of 7.5; no CVE id is provided in the text.

ZDI Published AdvisoriesAug 13, 05:00 UTC · Aug 13, 2026Advisory1

ZDI-26-686: Linux Kernel nftables Race Condition Local Privilege Escalation Vulnerability

ZDI discloses CVE-2026-74565, a CVSS 7.8 nftables race condition letting local low-privileged attackers escalate privileges on Linux.

ZDI-26-686 describes a race condition in the Linux Kernel nftables subsystem that allows local attackers to escalate privileges. Exploitation requires the ability to execute low-privileged code on the target system. ZDI assigned a CVSS rating of 7.8 and CVE-2026-74565.

ZDI Published AdvisoriesSep 14, 05:00 UTC · 2d agoAdvisory CVE-2026-74565

ZDI-26-695: Linux Kernel NFSv4 Server Race Condition Remote Code Execution Vulnerability

ZDI-26-695: Linux Kernel NFSv4 server race condition (CVE-2026-89688, CVSS 8.5) enables remote code execution on nfsd systems with authentication.

ZDI advisory ZDI-26-695 describes a race condition in the Linux Kernel NFSv4 server tracked as CVE-2026-89688 with a CVSS score of 8.5. Remote attackers can execute arbitrary code, but authentication is required and only systems with nfsd enabled are vulnerable. No in-the-wild exploitation is mentioned in the advisory.

ZDI Published AdvisoriesSep 14, 05:00 UTC · 2d agoVulnerability CVE-2026-89688

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 · 5d agoResearch1

Evaluating the NIST Bugs Framework Against CWE as a Successor for Automated Vulnerability Classification

NIST Bugs Framework evaluation shows it is more structured and automation-friendly than CWE for automated vulnerability classification, with gaps in attribute guidance.

The paper evaluates NIST SP 800-231's Bugs Framework (BF) against CWE as a target for automated CVE classification using a systematically screened corpus of CVE-to-CWE research. An inter-rater study with 2 subject-matter experts mapping 13 CVEs showed strong agreement on BF's cause and operation axes but only fair agreement on the attribute axis. Automated classification was tested across two LLM deployments under different budgets, and findings support BF as more structured and automation-friendly than CWE, though gaps include under-specified attribute guidance and missing fix commits for closed-source software.

arXiv cs.CRSep 14, 23:18 UTC · 1d agoResearch1

ZDI-26-568: Linux Kernel Net Scheduler Race Condition Local Privilege Escalation Vulnerability

ZDI disclosed a race condition (CVSS 7.5) in the Linux kernel net scheduler enabling local privilege escalation; no CVE assigned in the advisory text.

ZDI advisory ZDI-26-568 describes a race condition in the Linux kernel's net scheduler that allows local attackers to escalate privileges on affected installations. Exploitation requires the attacker to first execute high-privileged code on the target system. ZDI assigned a CVSS rating of 7.5; no CVE identifier is listed in the advisory text.

ZDI Published AdvisoriesAug 13, 05:00 UTC · Aug 13, 2026Vulnerability

ZDI-26-593: NVIDIA TensorRT ONNX File Parsing Heap-based Buffer Overflow Remote Code Execution Vulnerability

ZDI disclosed a second TensorRT heap-based buffer overflow RCE (CVE-2026-24268, CVSS 7.8) in ONNX file parsing, requiring user interaction.

The Zero Day Initiative published advisory ZDI-26-593 covering another heap-based buffer overflow in NVIDIA TensorRT's ONNX file parsing. A remote attacker can execute arbitrary code if the target opens a malicious file or visits a crafted page. ZDI rated the vulnerability CVSS 7.8 and assigned CVE-2026-24268.

ZDI Published AdvisoriesAug 24, 05:00 UTC · 23d agoAdvisory CVE-2026-24268

Disruptive cyber activity highlights risk from internet-exposed systems and edge devices

NCSC links disruptive cyber activity to internet-exposed systems and edge devices, urging OT owners to fix avoidable vulnerabilities and improve resilience.

The UK's NCSC issued guidance following disruptive cyber activity that highlights the risk to internet-exposed systems and edge devices. It encourages owners of operational technology to remediate avoidable vulnerabilities and invest in long-term cyber resilience. The notice underscores that exposed edge devices remain a common entry point for attackers against OT environments.

NCSC UKAug 27, 12:00 UTC · 20d agoAdvisory in the wild

ZDI-26-691: Linux Kernel Netlink-based Wireless Configuration Integer Overflow Local Privilege Escalation Vulnerability

ZDI-26-691: integer overflow in Linux kernel netlink wireless configuration (CVE-2026-53182) allows local privilege escalation by attackers already running high-privileged code; CVSS 8.2.

ZDI advisory ZDI-26-691 discloses an integer overflow in the Linux kernel's netlink-based wireless configuration that allows local attackers to escalate privileges on affected installations. Exploitation requires the attacker to first obtain the ability to execute high-privileged code on the target system. ZDI assigned a CVSS rating of 8.2 and CVE-2026-53182.

ZDI Published AdvisoriesSep 14, 05:00 UTC · 2d agoVulnerability CVE-2026-53182

ZDI-26-684: Linux Kernel KSMBD Query Directory Request Race Condition Remote Code Execution Vulnerability

ZDI discloses CVE-2026-64397, a CVSS 9.0 unauthenticated remote code execution race condition in Linux Kernel KSMBD.

ZDI-26-684 describes a race condition in the Linux Kernel KSMBD subsystem's Query Directory Request handling that allows unauthenticated remote attackers to execute arbitrary code. Only systems with KSMBD enabled are vulnerable. ZDI assigned a CVSS rating of 9.0 and CVE-2026-64397.

ZDI Published AdvisoriesSep 14, 05:00 UTC · 2d agoAdvisory CVE-2026-64397

Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.

The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:43 UTC · 5d agoAI research