DNGInspector Structural Analyzer for DNG/TIFF Metadata and IFD Anomaly Detection

This Python script implements a static inspection tool for Digital Negative DNG files by parsing the TIFF-based header and analyzing Image File Directory IFD entries for structural anomalies. The tool validates basic header fields, traverses IFD records, and flags suspicious metadata patterns suc...

DNGBehaviorAnalyzer Telemetry-Based DNG/TIFF Metadata Parser and Anomaly Detection

This Python script provides a telemetry-driven analysis framework for inspecting Digital Negative DNG files through low-level TIFF metadata parsing and runtime event logging. The tool reads and validates TIFF headers, traverses Image File Directory IFD entries, and records parser activity using...

SwarmSense-DNN: A Trustworthy and Decentralized Neural Framework for Proactive Anomaly Defense in Consumer IoT

The rapid growth of consumer IoT devices has introduced unprecedented challenges in trustworthy anomaly detection against AI-enabled cyber threats, requiring real-time, privacy-preserving, and scalable defense mechanisms. Traditional centralized strategies face critical limitations, including...

An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response

University Academic Management Information Systems ACMIS are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and academic integrity violations. Traditional rule-based intrusion detection syste...

CVE-2026-38950

An issue in ESA AnomalyMatch before 1.3.1 allow attackers to execute arbitrary code via crafted model checkpoint files. The affected components load model files from session directories using torch.load with unrestricted deserialization...

CVE-2026-5943

Document structural anomalies caused inconsistencies between page element relationships and internal index states. When scripts triggered document modifications, object reference validity was not properly maintained, leading to a crash when accessing an invalid pointer during page information...

CLIF: Cross-Layer LEO-ISL Fingerprinting for Physical and Network Attack Detection in Dense LEO Constellations

Low-Earth Orbit LEO mega-constellations such as Starlink by SpaceX and Kuiper by Amazon rely on optical Inter-Satellite Links ISLs for autonomous mesh routing to provide low-latency telecommunication, Internet of Things IoT, and security services globally. As commercial operators and governments...

NLLog: Lightweight, Explainable SOC Anomaly Detection Via Log-To-Language Rewriting

System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension. We present NLLog Natural-Language Log, a lightweight pipeline that deterministically rewrites parsed templates into WHO-WHAT-SEVERITY sentences, pools...

SECUREVENT: Hybrid AI/ML Security Monitoring for Distributed Event-Based Systems

Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservices, and security operations pipelines. Their loose coupling and asynchronous delivery improve scalability, but they also expand the attack surface:...

AnomalyMatch 安全漏洞

AnomalyMatch is a semi-supervised image anomaly detection tool open source by the European Space Agency. Versions of AnomalyMatch prior to 1.3.1 contained security vulnerabilities. These vulnerabilities stemmed from the use of torch.load to load model files without proper deserialization...

FALCON-C: Flow-Based Analysis and Labeling for Connected Vehicular Network Cybersecurity

Along with the recent rise in popularity of Electric Vehicles EVs, Electric Vehicle Supply Equipment EVSE has emerged as a new target for cyber attacks. Therefore, ensuring the security and integrity of network communication between EVSE components and vehicular clients is a significant challenge...

CVE-2026-6405

The Anomify AI – Anomaly Detection and Alerting plugin for WordPress is vulnerable to Cross-Site Request Forgery CSRF leading to Stored Cross-Site Scripting XSS in versions up to and including 0.3.6. This is due to missing nonce verification on the settings page handler and insufficient output...

WordPress Anomify AI – Anomaly Detection and Alerting plugin <= 0.3.6 - Cross-Site Request Forgery vulnerability

Cross-Site Request Forgery vulnerability discovered by Muhammad Nur Ibnu Hubab Ibnu - Pondok Teknologi in WordPress Plugin Anomify AI – Anomaly Detection and Alerting versions = 0.3.6...

WordPress Anomify AI – Anomaly Detection and Alerting plugin <= 0.3.6 - Authenticated (Administrator+) Stored Cross-Site Scripting vulnerability

Authenticated Administrator+ Stored Cross-Site Scripting vulnerability discovered by Muhammad Nur Ibnu Hubab Ibnu - Pondok Teknologi in WordPress Plugin Anomify AI – Anomaly Detection and Alerting versions = 0.3.6...

Advance_WAF_project_CS

WAFinity - Infinite Protection, Intelligent Detection WAFin...

Characterizing AI-Assisted Bot Traffic in Darknet Data: Implications for ICS and IIoT Security

The rise of automated scanning tools and AI assisted reconnaissance agents has significantly altered internet background traffic patterns, threatening the baseline assumptions underlying intrusion detection systems IDS deployed in critical infrastructure networks. This paper characterizes the...

When Prompts Become Payloads: A Framework for Mitigating SQL Injection Attacks in Large Language Model-Driven Applications

Natural language interfaces to structured databases are becoming increasingly common, largely due to advances in large language models LLMs that enable users to query data using conversational input rather than formal query languages such as SQL. While this paradigm significantly improves usabili...

AI Native Asset Intelligence

Modern security environments generate fragmented signals across cloud resources, identities, configurations, and third-party security tools. Although AI-native security assistants improve access to this data, they remain largely reactive: users must ask the right questions and interpret...

Evaluating Tabular Representation Learning for Network Intrusion Detection

Classic Network Intrusion Detection Systems NIDS often rely on manual feature engineering to extract meaningful patterns from network traffic data. However, this approach requires domain expertise and runs counter to the widely adopted principle of modern machine learning and neural networks: tha...

Exploit for Improper Check for Unusual or Exceptional Conditions in Mozilla Firefox

🔐 PDFGuardian Pro - Advanced PDF.js Security Fortification Fra...