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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 SecuritySep 16, 07:40 UTC · 1d agoAdvisory 2 sources

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

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 · 1d agoAdvisory1

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

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

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 · 16d agoIndustry

CISA and NIST Issue Guidance to Protect Cloud Identity Tokens

CISA and NIST published Interagency Report 8587 with voluntary guidance to harden cloud identity tokens against theft, forgery, and lateral movement.

CISA and NIST released NIST Interagency Report 8587 on September 15 with final voluntary guidance for federal agencies, cloud providers, and their customers on protecting SSO, federation, and API tokens. Requirements include one-hour maximum token lifetimes, 90-day signing key rotation for high-impact systems, hardware-backed key storage, explicit audience fields, and keeping tokens out of logs. The guidance was motivated by the 2020 ADFS compromise where forged SAML assertions bypassed MFA, and an incident where a leaked consumer signing key enabled token forgery and theft of 60,000+ emails from one agency. Nearly 250 public comments shaped the text, with input from Google, Microsoft, Okta, AWS, Oracle, IBM, HashiCorp, Wiz, and the OpenID Foundation via the Joint Cyber Defense Collaborative.

Infosecurity MagazineSep 16, 14:00 UTC · 19h agoAdvisory

AI agent authorization risks remain a gap in new NIST-CISA token security guidance

NIST and CISA release IR 8587 guidance on securing signed tokens, but AI agent authorization and delegation risks remain out of scope.

NIST, with CISA support, published 'Protecting Tokens and Assertions from Forgery, Theft, and Misuse' (NIST IR 8587), recommending continuous monitoring and tighter token lifecycle controls for SSO and API access. The guidance does not yet fully address AI agent identity, delegation chains, or prompt injection steering agents with valid tokens, and NIST says new or expanded standards are needed. Experts recommend treating AI agents as low-trust non-human identities, maintaining agent inventories, expiring credentials after task completion, and requiring human approval for high-risk actions. The report references shared-signal mechanisms like CAEP and RISC, and follows a May incident where a CISA contractor GitHub repository exposed AWS and GitHub tokens.

CSO OnlineSep 16, 15:42 UTC · 17h agoAdvisory

Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM

French BabyLM entry METRON-FR (125M GPT-2, 92.47M words) shows tokenizer artifacts dominate child-scale zero-shot evaluation; proposes standard diagnostics.

METRON-FR is a 125M-parameter GPT-2 pretrained on 92.47M French words, submitted to the BabyLM 2026 Strict track, scoring 85.97% on the native Quebec-French QFrBLiMP benchmark and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol combining French task-data translation with rank-16 LoRA shows relational tasks gain while world-knowledge tasks regress. Bilingual Lexicon Induction reaches p@1 of 68.84%, 18x above chance, and ablations show single-token zero-shot scoring is dominated by tokenizer and template artifacts at child scale.

arXiv cs.AI / cs.LG / cs.CLSep 15, 16:43 UTC · 1d agoAI research

Akamai a Strong Performer in 2026 Gartner Peer Insights™ Report for SEB

Akamai named a Strong Performer in the 2026 Gartner Peer Insights report for Secure Email Boundary solutions.

Akamai announced it was recognized as a Strong Performer in the 2026 Gartner Peer Insights report for Secure Email Boundary (SEB). No additional article details were available beyond the announcement title.

Akamai BlogSep 15, 12:00 UTC · 1d agoIndustry

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

Path to Astra: critical capabilities and frontier safeguards

OpenAI says Astra is its first model to meet the Critical cybersecurity capability threshold under the Preparedness Framework, launching with stronger safeguards.

OpenAI published 'Path to Astra', announcing that Astra is the first OpenAI model to meet the Critical cybersecurity capability threshold under its Preparedness Framework. The company says the model will be released with strengthened frontier safeguards, marking a milestone in frontier capability and safety evaluation.

OpenAI NewsSep 1, 13:00 UTC · 15d agoModel release

Cohere Releases North Small Translate: A 218B MoE Translation Model That Scores 83.6 on WMT26 Across 50 Languages

Cohere released North Small Translate, an open-weight 218B MoE (25B active) translation model scoring 83.6 on WMT26 across 50 languages.

Cohere and Cohere Labs released North Small Translate, a decoder-only sparse Mixture-of-Experts translation model with 218B total and 25B active parameters, 128 experts with 8 activated per token plus shared experts, and 16K-token input and output context. In Cohere's vendor-reported WMT26 evaluation, judged by GPT-5.6-Sol, it scores 83.6 averaged across 50 languages (84.36 in an agentic multi-pass mode), ahead of DeepL NextGen (81.37), Qwen 3.5 397B A17B (81.56), GLM 5.2 (76.50), and Google Translate (68.20). The model was built with RWS's Language Weaver team, post-trained specifically for translation, and reports 112 output tokens per second versus 81 for Gemma 4 31B, with long-document xCOMET-XL scores of 48.9 versus 21.3 for Google Translate. It is available free on Cohere's Chat V2 API until rate limits, with three self-hosting checkpoints including a 4-bit NVFP4 variant running on 1x B200 or 2x H100.

MarkTechPostSep 11, 06:57 UTC · 6d agoModel release

N0va Phishkit Targets US and EU Businesses: A New Challenge for Identity Security

N0va phishkit targets US and EU organizations with trusted-brand lures, capturing tokens via legitimate authentication flows to gain SSO access to corporate resources.

The N0va phishing kit targets organizations in government, technology, consulting, and healthcare across North America and Europe with lures impersonating Microsoft Teams, SharePoint, OneDrive, DocuSign, Google Drive, Dropbox, Zoom, and Adobe Sign. Victims are guided through legitimate authentication flows, including device code phishing, after which N0va captures access and refresh tokens and abuses token-exchange or device-registration mechanisms to establish SSO access. ANY.RUN tracks the campaign via a characteristic /api/verification/init URL pattern and demonstrates detection in its interactive sandbox.

The Hacker Newsupdated Sep 16, 14:54 UTC · 18h agofirst Sep 16, 11:58 UTC · 21h agoPhishing & fraud in the wild 9 sources2

Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model

Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.

The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:43 UTC · 6d agoAI research

Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue

Researchers introduce Motion-Omni, an end-to-end model generating speech with synchronized full-body motion, responding 5.4x faster than cascade pipelines.

Motion-Omni is an end-to-end framework in which a spoken dialogue model outputs facial expressions and hand, upper-body, and lower-body motion directly from the hidden states that produce speech, replacing two-stage cascade pipelines. Trained on 422,856 quality-ranked pseudo-labeled pairs (1,402 hours) with a Qwen2.5-7B-Instruct backbone, Motion-Omni-Q7 matches its teacher cascade within 2% on reference-free motion metrics, achieves a 2.62% word error rate, and runs faster than real time (RTF=0.78). The authors also release the SwDA-500 dataset and the first public evaluation protocol for stochastic open-ended full-body spoken dialogue.

Hugging Face daily papersAug 27, 20:00 UTC · 20d agoAI research2

NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

Hugging Face daily papersSep 8, 20:00 UTC · 8d agoAI research1

11 Best CSPM Tools Compared (2026): Features & Pricing

CSPM comparison ranks Wiz first for agentless attack-path analysis; notes Ermetic absorbed into Tenable and Lacework into Fortinet FortiCNAPP.

An editorial comparison of eleven CSPM tools ranks Wiz as the agentless attack-path momentum leader, Prisma Cloud as the breadth benchmark, and Orca as the agentless SideScanning pioneer. It highlights consolidation: Ermetic now powers Tenable Cloud Security and Lacework became Fortinet's FortiCNAPP. The guide recommends starting with free tiers from Defender for Cloud, Prowler, and native cloud tools before buying.

GBHackersSep 15, 06:37 UTC · 2d agoIndustry1

Realtime-Venus: A full-duplex interaction system with asynchronous delegation

Realtime-Venus introduces two 9B full-duplex interaction models (Omni and Audio) that outperform Gemini 3.1 Live and GPT-4o on continuation metrics.

Realtime-Venus is a proactive full-duplex interaction system built on two separately trained 9B models: Realtime-Venus-Omni for audio-visual interaction and Realtime-Venus-Audio for spoken interaction. A dual-loop runtime lets foreground interaction continue while Realtime-Venus-Harness asynchronously executes background reasoning and tool tasks. Realtime-Venus-Omni leads on six of eight video benchmarks, including StreamingBench (70.2%), OVO-Bench (64.7%), and Daily-Omni (81.3%), while Realtime-Venus-Audio tops MMAU (78.0%) and MMAU-Pro (63.2%). On Full-Duplex-Bench v1.5, Realtime-Venus-Audio handles 75% of interruptions and exceeds Gemini 3.1 Live and GPT-4o on all three continuation metrics.

Hugging Face daily papersSep 11, 20:00 UTC · 5d agoAI research

RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any Inputs

RelateAnything is a 53M-parameter open-vocabulary relation prediction model running at 20 ms/frame, with 2.3-3.5x higher mean recall than comparable open-vocabulary methods.

RelateAnything predicts scored relations between image regions using any predicate vocabulary supplied at inference as text embeddings, with object labels never required as input, so region sources can change without retraining. Training covers 19,103 predicates using positive-unlabeled supervision; the authors release RA-4M (474k images, 4.3M geometrically verified relations over 10,102 free-text predicates) and the OV-SGG-Bench evaluation suite. The 53M-parameter model runs at 20 ms/frame and achieves 2.3-3.5x the mean recall of the strongest comparable open-vocabulary method across cross-dataset and zero-shot benchmarks. Model, corpus, and benchmark are public.

Hugging Face daily papersSep 10, 20:00 UTC · 6d agoAI research1

Zero-Day Dominance: How Akamai Defends Before the Industry Discloses

Akamai published vendor marketing on how its platform defends customers against zero-day exploits before industry-wide disclosure.

Akamai published a blog post titled 'Zero-Day Dominance: How Akamai Defends Before the Industry Discloses'; no article body text was available. Based on the title, the piece promotes Akamai's capabilities for protecting customers from zero-day exploits ahead of public disclosure. No specific vulnerabilities, CVEs, incidents, or customers are identified.

Akamai BlogAug 28, 12:00 UTC · 19d agoIndustry

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 · 23d agoAdvisory

NeoMME: an efficient Multimodal-native and Multilingual Encoder

H Company released NeoMME, an efficient multimodal-native and multilingual encoder, via a post on the Hugging Face blog.

H Company published a Hugging Face blog post introducing NeoMME, described as an efficient multimodal-native and multilingual encoder. The article body was unavailable in the feed, so architecture, benchmarks, and licensing details could not be verified. The post suggests a new encoder release relevant to multilingual multimodal model development.

Hugging Face BlogSep 3, 13:13 UTC · 13d agoModel release1

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 OnlineSep 10, 09:00 UTC · 7d agoResearch

Higher-order pruning of experts in mixture-of-experts language models

New HOPE method uses second-order objectives to prune Mixture-of-Experts LLMs, outperforming REAP at 50% pruning on models up to 122B parameters.

Researchers derive HOPE (Higher-Order Pruning of Experts), a second-order pruning objective for Mixture-of-Experts language models that provably minimizes an upper bound on pruning error by modeling cooperative expert interactions. They show REAP, a state-of-the-art first-order method, is a special case of HOPE with interaction terms ignored. Across three frontier MoE models up to 122B parameters, two calibration sets, and benchmarks covering math, instruction following, coding, and agentic tasks, HOPE achieves the best average rank (1.58 of 5 at 50% pruning versus 2.42 for REAP), with gains up to +6.1% on agentic coding.

arXiv cs.AI / cs.LG / cs.CLSep 16, 16:55 UTC · 16h agoAI research

Merchants of Insecurity

Opinion essay titled Merchants of Insecurity, apparently critiquing security industry practices; no article body was included in the feed.

The feed provides only the title and a comments link for this Lobsters-shared post from blog.happyfellow.dev; no article text was available. Based on the title, it appears to be an opinion piece about incentives in the security industry, possibly arguing vendors profit from perpetuating insecurity.

Lobsters · securityAug 26, 14:30 UTC · 21d agoIndustry

Wiz achieves GovRAMP High Authorization

Wiz for Gov achieved GovRAMP High authorization, validating NIST SP 800-53r5 controls for state, local government, and higher-education cloud security procurement.

Wiz announced that Wiz for U.S. Government has officially achieved GovRAMP High authorization, validating it against NIST SP 800-53r5 security controls required for highly sensitive state data and critical infrastructure. The 'verify once, serve many' designation is aimed at state, local government, and higher education procurement. Wiz positions its CNAPP as providing code-to-runtime visibility, risk prioritization, compliance automation, and AI pipeline security. Existing Wiz for Gov customers automatically inherit the high-baseline controls without migration.

Wiz BlogSep 10, 11:00 UTC · 6d agoIndustry1