Search: “risk-framework”

Toward an Empirical Probabilistic Risk Manifestation Model of Organizational Cybersecurity in SMEs

Empirical study of 22 SME security assessments builds a probabilistic risk model and shows assessments can be cut 24-45% while retaining most critical findings.

Researchers analyzed 281 validated security findings from 22 real-world SME cybersecurity assessments conducted over two years via a pro bono university clinic. They derived an empirical Risk Manifestation Model linking eight organizational security functions to two exposure conditions, five attack mechanisms, and six outcome categories, using probability propagation to identify dominant risk pathways. The dominant pathway runs from asset exposure to credential compromise to unauthorized access, stable under leave-one-organization-out analysis. Retaining six functions reduces assessment burden by 24% while preserving 97% of critical findings; five functions cut burden 45% while preserving 89% of critical findings.

arXiv cs.CR·Sep 14, 01:15 UTC · 2d agoResearch

Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment

Researchers audit 31 NLP techniques for clinician-annotated suicide risk prediction, finding only 5 of 31 comparisons yield reliable gains.

A study of 1,635 clinician-annotated social media posts ran roughly 300 controlled experiments across 7 methodological families, auditing techniques such as model scaling, synthetic data, ensembling, and threshold tuning under severe class imbalance. The proposed system reformulates risk factor prediction as entailment between posts and codebook definitions, using architecturally diverse ensembles with class-balanced training and deployment-consistent calibration. It scores 0.8203 for risk, 0.7953 for evidence, and 0.7045 macro-F1 for factors, ranking third among 53 teams.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:14 UTC · 8d agoAI research

From ‘High/Medium/Low’ to Dollars: Making Cyber Risk Legible to Your CFO

Cyble argues security teams should express cyber risk in financial terms for CFOs instead of high/medium/low ratings, citing its 2025 threat forecast results.

Cyble published guidance on cyber risk quantification, arguing qualitative high/medium/low ratings fail to convey financial exposure to executives. The piece notes that over 80% of its 2025 threat predictions, including AI-driven ransomware and supply-chain attacks, materialized as anticipated.

Cyble·Aug 25, 09:45 UTC · 22d agoIndustry

Threat Matrix: Mapping threats across cloud web applications

Microsoft released a Cloud Web Applications Threat Matrix, a MITRE ATT&CK-aligned framework mapping threats to cloud-hosted web apps and serverless platforms.

Microsoft introduced the cloud web applications threat matrix, which organizes attack techniques for cloud-hosted web apps and serverless platforms using MITRE ATT&CK tactics, from resource development through impact. The framework covers attack paths spanning application code, managed runtimes, workload identities, and deployment pipelines, cataloging techniques such as subdomain takeover, code injection in connected repositories, compromised registry images, exposed admin interfaces, and serverless trigger injection. It builds on Microsoft's earlier Kubernetes and storage services matrices to help defenders identify visibility gaps, prioritize hardening, and plan investigations in cloud-native environments.

Microsoft Security Blog·Sep 9, 21:30 UTC · 6d agoTools

Introducing Unit 42’s Attribution Framework

Unit 42 releases its Attribution Framework, a systematic method using Diamond Model and Admiralty scores to attribute activity clusters to named threat actors.

Palo Alto Networks' Unit 42 introduced a structured framework for threat actor attribution built on the Diamond Model of Intrusion Analysis and Admiralty reliability/credibility scoring. The framework tracks activity at three levels: activity clusters (named CL-STA, CL-CRI, CL-UNK, or CL-MIX), temporary threat groups, and named threat actors using the constellation naming schema. Analysts score evidence across TTPs, tooling, malware code, OPSEC, infrastructure, timelines, and victimology to decide when to merge or elevate clusters, avoiding premature group naming.

Palo Alto Unit 42·Aug 17, 10:52 UTC · 29d agoResearch

Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal

A self-distillation safety framework tunes narrow-boundary refusals in Qwen3-8B, raising target-domain refusal to 84.75% while cutting over-refusal from 15.20% to 5.20%.

The paper formulates narrow-boundary safety, where deployments need refusals within specific topics rather than whole subjects, and proposes an offline self-generated framework with controlled topic generation, escalating retries, and harmful-benign boundary pairs. On political persuasion with Qwen3-8B, the method raised target-domain refusal from 9.47% to 84.75% and cut the mean unsafe-response rate across three broader benchmarks from 26.26% to 0.14%. Verified target-model responses reduced over-refusal from 15.20% to 5.20%, and boundary-pair data cut comply-side over-refusal on held-out pairs from 32.94% to 4.16%. Results show data composition controls the safety-usability trade-off and alignment should be evaluated on both sides of the refusal boundary.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI safety & security1

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.CR·Sep 14, 23:18 UTC · 1d agoResearch1

AI vulnerability discovery scores the highest impact of 20 emerging risks

Gartner survey of 316 organizations ranks AI-driven vulnerability discovery as the top emerging risk, with tangible impact expected within roughly two years.

Gartner's quarterly survey had 316 risk managers, auditors, and senior executives rank 20 emerging threats in April and May, with AI discovery of cyber vulnerabilities ranked first, up from outside the top five the prior quarter. Respondents scored the impact time frame at 1.92 on a scale where 1 means under a year and 2 means one to two years, and 76% placed it in their top ten, ranking first in all four regions and highest among banking and financial respondents at 78%. The analysis notes AI now finds unknown flaws at volumes patching teams cannot absorb and that exploit development time has collapsed, citing defensive efforts like Anthropic's Project Glasswing and OpenAI's Daybreak. Gartner recommends recalibrating cyber risk impact, revisiting risk appetite, demanding stronger vendor security validation, and moving toward faster automated remediation.

Help Net Security·Aug 26, 00:00 UTC · 21d agoIndustry

OpenAI locks down Astra over potential critical cyber capabilities

Help Net Security·Aug 10, 00:00 UTC · Aug 10, 2026Vulnerability

Causal Foundation Models

A paper introduces causal foundation models (CFMs): pretrained networks that estimate treatment effects on new datasets via in-context learning without fine-tuning.

Causal foundation models (CFMs) apply the foundation-model paradigm to causal inference, replacing bespoke per-problem estimator pipelines with networks pretrained once at scale. CFMs estimate causal quantities such as the average treatment effect on entirely new datasets through in-context learning, without model updates. The work serves as a practical introduction to the emerging area, covering background in causal inference and machine learning and including example code and Jupyter notebooks.

Hugging Face daily papers·Sep 1, 20:00 UTC · 14d agoAI research

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

TIER: Threat Implicitness Benchmark for Evaluating LLM Safety Behaviors

TIER benchmark shows LLM safety behaviors shift gradually across threat implicitness levels, with jailbreaks exposing the largest robustness gaps.

The TIER benchmark evaluates LLM safety behaviors across four risk domains and four threat levels, from explicit harmful requests to sophisticated jailbreaks, using a six-label behavior scale and two independent LLM judges. Experiments on six open-weight LLMs show safety behaviors evolve gradually across threat levels rather than flipping from refusal to compliance. Models with similar Attack Success Rates can exhibit distinct response distributions, arguing for behavior-aware safety evaluation.

arXiv cs.CR·Sep 4, 13:13 UTC · 11d agoAI safety & security

One in four MCP servers opens AI agent security to code execution risk

Noma Security whitepaper finds most popular AI Skills and many MCP servers carry high-risk capabilities, with state changes most prevalent.

Noma Security analyzed hundreds of popular MCP servers and Skills across eight risk categories, finding most widely used Skills carry at least one risky characteristic and a typical enterprise runs well over a hundred high-risk agent tools, with arbitrary code execution common across MCP servers. The most prevalent risk is the ability to change state or data, and named toxic combinations include ContextCrush data leakage, ForcedLeak via poisoned Salesforce CRM records, DockerDash supply-chain compromise, the Replit production database deletion, and the hijacked Amazon Q VS Code extension. Building on OWASP LLM06:2025, the paper proposes the No Excessive CAP framework of capabilities, autonomy, and permissions, recommending allowlisting, MCP version pinning, approval gates on irreversible actions, and user-scoped expiring credentials.

Help Net Security·Aug 24, 04:21 UTC · 23d agoAI safety & security

Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:57 UTC · 5d agoAI research

Safe Meta-Reinforcement Learning via Information Space Reachability

Safe meta-RL framework reasons about safety in information space, learning a safety value function used for safety filtering and constrained policy optimization.

The paper proposes safe meta-RL that reasons about safety in information space, capturing both physical state and the agent's belief over the underlying task. A safety value function measures the probability of avoiding unsafe regions indefinitely and satisfies a self-consistency condition and Bellman equation, making it learnable via meta-RL. The resulting algorithm uses the learned function for safety filtering and constrained policy optimization, with effectiveness demonstrated on meta-RL benchmarks.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:27 UTC · 1d agoAI research

Building a risk-based vulnerability management program that scales

Asimily CEO Shankar Somasundaram outlines a risk-based vulnerability management approach using inventory, attack paths, KEV and EPSS data.

In a Help Net Security video, Asimily CEO Shankar Somasundaram argues patching everything is infeasible as AI-driven attacks inflate vulnerability counts, with one customer finding a thousand unknowns for each known one. He recommends building a full inventory of devices, applications, and data flows, mapping attack paths for reachability, and prioritizing with KEV, EPSS, and business impact. Mitigations include patching, virtual patching via NACs and firewalls, segmentation, and configuration snapshots to detect drift.

Help Net Security·Aug 24, 04:21 UTC · 23d agoIndustry

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.CL·Sep 10, 17:43 UTC · 5d agoAI research

Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization

ModerationBench shows foundation models can nearly triple Bluesky's moderation F1 (0.60 vs 0.22), with instruction- and example-driven guidance performing comparably.

Researchers built ModerationBench, a new benchmark of 4,000 manually annotated in-the-wild posts from Bluesky, to test whether foundation models can reliably operationalize content moderation policies. They systematically compare instruction-driven guidance (reasoning from policy precepts) with example-driven guidance (generalizing from precedents) for Vision-Language Models. Both paradigms achieve comparable peak effectiveness, and foundation models nearly triple the F1 of Bluesky's deployed moderation system on Random Posts (0.60 vs 0.22).

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:30 UTC · 6d agoAI research1

Propagation Model for SSC attacks: Why SBOM (tools) don't tell the whole truth

Study shows open-source SBOM tools only cover structural exposure and vulnerability presence, missing code reachability and taint-path analysis stages.

An arXiv paper proposes a four-stage propagation model for software supply chain attack effects and empirically evaluates four open-source SBOM tools against it using three projects and the Log4j vulnerability as the test case. Current SBOM tools systematically support only Stage 1 (structural exposure) and Stage 2 (vulnerability class presence), while Stage 3 (code reachability) and Stage 4 (taint path analysis) require capabilities absent from the SBOM ecosystem. The authors argue propagation-centred SSC security research is needed to prevent cyber risk from evolving into systemic risk.

arXiv cs.CR·Sep 4, 17:31 UTC · 11d agoResearch1

Target leakage, not model class, explains reported accuracy in survey-based cardiovascular screening: a leakage-tiered audit of glass-box and tabular foundation models

Audits of 10 classifiers on BRFSS show target leakage, not model class, drives the reported 0.89 AUROC in survey-based cardiovascular screening.

The study benchmarks ten model classes, including glass-box and tabular foundation models, for prevalent myocardial infarction on 442,067 respondents of the 2022 BRFSS across five feature tiers of decreasing leakage risk. Removing two post-diagnostic features costs every model 0.049-0.051 AUROC and collapses performance into a 0.0045-wide band, and the explainable boosting machine matches all alternatives within 0.005 while scoring roughly 104x faster than the strongest foundation model. Frozen models transport within 0.002 AUROC to 2023 data; the authors conclude evaluation practice and feature sets, not model capacity, are the binding constraint.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:29 UTC · 5d agoAI research1

Recorded Future Launches Digital Risk Protection, Unifying Brand and Identity Monitoring

Recorded Future launched Digital Risk Protection, unifying brand and identity monitoring across five external threat surfaces in one workflow.

Recorded Future announced Digital Risk Protection, combining brand threat monitoring and identity exposure monitoring across five use cases: malicious site, impersonation, code repository, dark web brand, and identity exposure monitoring. The platform includes an AI Triage Agent that automates alert evaluation with explicit verdicts and context, expanding social media analysis, OCR, full Telegram coverage, and infostealer log ingestion. Gartner's 2026 Magic Quadrant folded digital risk protection into cyber threat intelligence technologies, and the launch cites $15.9 billion in 2025 US fraud/scam losses, up 28% year over year.

Recorded Future·Sep 9, 00:00 UTC · 7d agoTools

K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.

K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 16:49 UTC · 1d agoAI safety & security

Quantifying IIoT Sensor Node Criticality by Fusing its Data Criticality and Security Vulnerability

Researchers propose a Dempster–Shafer framework fusing IIoT sensor data criticality with CVSS 4.0/3.1 vulnerability scores to rank node criticality.

The paper introduces a framework that evaluates Industrial IoT sensor node criticality by fusing data criticality and cybersecurity vulnerability scores using Dempster–Shafer (D-S) theory. It was validated on a dataset from red wine production and is claimed to generalize to other industrial settings with minimal modification. Results show criticality rankings derived from CVSS 4.0 scores differ significantly from those derived from CVSS 3.1, underscoring how vulnerability scoring methodology affects security prioritization.

arXiv cs.CR·Sep 9, 07:03 UTC · 7d agoResearch

How to build an exposure management program the business trusts: Lessons from Tenable’s CSO

Tenable's CSO describes an AI-driven exposure management program that consolidates tool sprawl and translates cyber risk for boards.

A Tenable blog post shares lessons from CSO Robert Huber on moving to an AI-driven exposure management program. It argues tool sprawl and data silos hinder holistic risk assessment and that exposure management unifies attack-surface data into business-level metrics for the C-suite.

Tenable Blog·Aug 27, 14:30 UTC · 19d agoIndustry

VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities

Introduces VEX-Bench, 75 expert-labeled real-world cases testing whether LLM agents can assess supply chain vulnerability exploitability; frontier models reach about 80% F1.

VEX-Bench is the first benchmark evaluating LLM agents on assessing whether upstream dependency vulnerabilities are exploitable in downstream projects, with 75 real-world expert-labeled cases across Python, Java, and Go mined from GitHub. Nine models across three agent harnesses were evaluated; GPT-5.5 and Claude Opus 4.6 reach approximately 80% F1 on binary vulnerability-status classification, but only GPT-5.5 surpasses 70% macro-F1 on fine-grained justification classification. The gap highlights the difficulty of moving beyond binary exploitability calls to explaining exploitability reasons, unlike prior benchmarks targeting zero-day settings.

arXiv cs.CR·Sep 7, 22:52 UTC · 8d agoResearch1

Financial Stability Board Sounds the Alarm Over Frontier AI Risks

The Financial Stability Board warned G20 banking leaders that frontier AI adoption poses growing cyber and financial stability risks.

The Financial Stability Board (FSB) issued a warning to G20 banking leaders about cyber and financial-stability risks stemming from frontier AI. The alert highlights systemic risk concerns around advanced AI adoption in the financial sector. No specific incidents or regulatory actions were announced.

Infosecurity Magazine·Sep 1, 09:20 UTC · 15d agoAI policy

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

New framework tests whether LLM-cited explanation factors are necessary or sufficient, finding weak correlation across Claude, GPT, and Gemini models.

An arXiv paper introduces black-box intervention tests measuring whether factors LLMs cite in their explanations are necessary or sufficient for their outputs in agent oversight workflows. Across eight models from the Claude, GPT, and Gemini families, Spearman correlations between cited rankings and measured influence ranged from 0.349-0.354 (advisor recommendation) to 0.431-0.580 (prompt monitoring). Uncited factors scored above the lowest cited factor in up to 57.6% of advisor responses, showing cited top-three factors do not reliably identify the most influential inputs.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:37 UTC · 11d agoAI safety & security1

Edge Case launches Guardian, an AI platform for tracking risk across autonomous systems

Edge Case launched Guardian, an AI platform creating a Digital Safety Twin to continuously track risk across autonomous and defense systems.

Edge Case launched Guardian, an AI-driven platform that fuses safety analysis, engineering data, and operational signals into a continuous Digital Safety Twin. It targets advanced autonomous platforms in commercial and defense markets, with six customer agreements signed at launch. The platform tracks hazards, mitigations, and source-linked evidence across the development lifecycle, shifting safety from one-time review to continuous capability.

Help Net Security·Sep 2, 00:00 UTC · 14d agoAI tools & infra

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 Magazine·Aug 24, 15:30 UTC · 22d agoAdvisory

The State of Cloud Risk 2026: Most Security Findings Aren’t Real Attacker Opportunities

Wiz Research's 2026 cloud risk report finds most high-severity findings lack a viable path to compromise, urging exposure-based prioritization.

Wiz Research telemetry underpins a State of Cloud Risk 2026 report arguing that the majority of high-severity security findings are not real attacker opportunities because they lack a path to compromise. The report pushes defenders to prioritize findings by exposure and exploitability rather than raw severity scores. No specific CVEs, victims, or incident counts are provided in the available text.

Wiz Blog·Aug 26, 15:01 UTC · 20d agoResearch