CVE-2026-35210

OpenCTI is an open source platform for managing cyber threat intelligence knowledge and observables. Prior to 7.260326.0, an authorization bypass vulnerability in OpenCTI allows any authenticated user with KNOWLEDGEKNUPDATE permission to bypass Confidence Level validation and Object Marking...

CVE-2026-35210 OpenCTI: Authorization Bypass via `synchronized-upsert` HTTP Header Injection

OpenCTI is an open source platform for managing cyber threat intelligence knowledge and observables. Prior to 7.260326.0, an authorization bypass vulnerability in OpenCTI allows any authenticated user with KNOWLEDGEKNUPDATE permission to bypass Confidence Level validation and Object Marking...

CVE-2026-35210

OpenCTI is an open source platform for managing cyber threat intelligence knowledge and observables. Prior to 7.260326.0, an authorization bypass vulnerability in OpenCTI allows any authenticated user with KNOWLEDGEKNUPDATE permission to bypass Confidence Level validation and Object Marking...

CVE-2026-35210

Summary: OpenCTI prior to version 7.260326.0 contains an authorization bypass via the synchronized-upsert: true HTTP header, exploitable by any authenticated user with KNOWLEDGE_KNUPDATE permission. This allows bypassing Confidence Level validation and Object Marking restrictions, enabling attack...

CVE-2026-35210 OpenCTI: Authorization Bypass via `synchronized-upsert` HTTP Header Injection

OpenCTI is an open source platform for managing cyber threat intelligence knowledge and observables. Prior to 7.260326.0, an authorization bypass vulnerability in OpenCTI allows any authenticated user with KNOWLEDGEKNUPDATE permission to bypass Confidence Level validation and Object Marking...

PT-2026-56601

Name of the Vulnerable Software and Affected Versions OpenCTI versions prior to 7.260326.0 Description An authorization bypass allows authenticated users with KNOWLEDGE KNUPDATE permission to circumvent Confidence Level validation and Object Marking restrictions. This is achieved by injecting the...

A Validated Prompt Bank for Malicious Code Generation: Separating Executable Weapons from Security Knowledge in 1,554 Consensus-Labeled Prompts

Existing benchmarks of language-model refusal on malicious-coding tasks routinely conflate requests for executable malicious software with requests for harmful security knowledge. This conflation matters because the two request types plausibly trigger distinct refusal pathways in safety-aligned...

False Security Confidence in Benign LLM Code Generation

Prior work has demonstrated that functionally correct yet vulnerable outputs arise systematically in threat-oriented settings, where adversarial or implicit channels are used to induce security failures in code agents and automated patching workflows. This note introduces a complementary but...

Exploit for Path Traversal in Vmware Cloud_Foundation

CTT-enhanced-VMware-vCenter Looking at current high-impact vul...

Decision-Aware Trust Signal Alignment for SOC Alert Triage

Detection systems that utilize machine learning are progressively implemented at Security Operations Centers SOCs to help an analyst to filter through high volumes of security alerts. Practically, such systems tend to reveal probabilistic results or confidence scores which are ill-calibrated and...

How to Avoid Phishing Incidents in 2026: A CISO Guide

Phishing in 2026 is harder to detect and verify. Learn how CISOs can speed up investigations, reduce noise, and respond with confidence...

Efficient Adversarial Malware Defense Via Trust-Based Raw Override and Confidence-Adaptive Bit-Depth Reduction

The deployment of robust malware detection systems in big data environments requires careful consideration of both security effectiveness and computational efficiency. While recent advances in adversarial defenses have demonstrated strong robustness improvements, they often introduce computationa...

Beyond the Checkbox: How Wiz Transforms SOC 2 into a Security Powerhouse

Turning compliance chaos into continuous confidence...

The Cybersecurity Perception Gap: Why Executives and Practitioners See Risk Differently

Does your organization suffer from a cybersecurity perception gap? Findings from the Bitdefender 2025 Cybersecurity Assessment suggest the answer is probably "yes" — and many leaders may not even realize it. This disconnect matters. Small differences in perception today can evolve into major blin...

Introducing TruConfirm for Enterprise TruRisk™ Management: Automated Exposure Validation

Enterprise security leaders and their teams face an impossible challenge: drowning in thousands of critical exposures in an ever-expanding attack surface while simultaneously trying to determine which ones pose a genuine risk of exploitation in their organizational environment. Traditional CVSS...

HumanSAM: Classifying Human-Centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly

Numerous synthesized videos from generative models, especially human-centric ones that simulate realistic human actions, pose significant threats to human information security and authenticity. While progress has been made in binary forgery video detection, the lack of fine-grained understanding ...

Optimal Debiased Inference on Privatized Data Via Indirect Estimation and Parametric Bootstrap

We design a debiased parametric bootstrap framework for statistical inference from differentially private data. Existing usage of the parametric bootstrap on privatized data ignored or avoided handling the effect of clamping, a technique employed by the majority of privacy mechanisms. Ignoring th...

SoK: Stablecoin Designs, Risks, and the Stablecoin LEGO

Stablecoins have become significant assets in modern finance, with a market capitalization exceeding USD 246 billion May 2025. Yet, despite their systemic importance, a comprehensive and risk-oriented understanding of crucial aspects like their design trade-offs, security dynamics, and...

Expert-In-The-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection

As cyber threats become more sophisticated, rapid and accurate vulnerability detection is essential for maintaining secure systems. This study explores the use of Large Language Models LLMs in software vulnerability assessment by simulating the identification of Python code with known Common...

Aurora: Are Android Malware Classifiers Reliable under Distribution Shift?

The performance figures of modern drift-adaptive malware classifiers appear promising, but does this translate to genuine operational reliability? The standard evaluation paradigm primarily focuses on baseline performance metrics, neglecting confidence-error alignment and operational stability...