CVE-2026-56149

Allocation of Resources Without Limits or Throttling CWE-770 in Elasticsearch can lead to a denial of service via Excessive Allocation CAPEC-130. A user with elevated privileges can submit a specially crafted machine learning request that causes excessive memory consumption, which may render the...

EUVD-2026-41072

Allocation of Resources Without Limits or Throttling CWE-770 in Elasticsearch can lead to a denial of service via Excessive Allocation CAPEC-130. A user with elevated privileges can submit a specially crafted machine learning request that causes excessive memory consumption, which may render the...

CVE-2026-56149 Allocation of Resources Without Limits or Throttling in Elasticsearch Leading to Denial of Service

Allocation of Resources Without Limits or Throttling CWE-770 in Elasticsearch can lead to a denial of service via Excessive Allocation CAPEC-130. A user with elevated privileges can submit a specially crafted machine learning request that causes excessive memory consumption, which may render the...

CVE-2026-56149

Allocation of Resources Without Limits or Throttling CWE-770 in Elasticsearch can lead to a denial of service via Excessive Allocation CAPEC-130. A user with elevated privileges can submit a specially crafted machine learning request that causes excessive memory consumption, which may render the...

CVE-2026-56149

Allocation of Resources Without Limits or Throttling CWE-770 in Elasticsearch can lead to a denial of service via Excessive Allocation CAPEC-130. A user with elevated privileges can submit a specially crafted machine learning request that causes excessive memory consumption, which may render the...

CVE-2026-6330

The ML-KEM ARM64 NEON ciphertext comparison only compares half of the input, breaking the Fujisaki-Okamoto transform's implicit rejection and weakening IND-CCA2 security on that code path. The constant-time comparison effectively ignored part of the re-encrypted ciphertext, so a decapsulating par...

CVE-2026-6330

The ML-KEM ARM64 NEON ciphertext comparison only compares half of the input, breaking the Fujisaki-Okamoto transform's implicit rejection and weakening IND-CCA2 security on that code path. The constant-time comparison effectively ignored part of the re-encrypted ciphertext, so a decapsulating par...

PT-2026-52603

Name of the Vulnerable Software and Affected Versions ML-KEM affected versions not specified Description An issue exists in the ARM64 NEON ciphertext comparison where only half of the input is compared. This failure breaks the implicit rejection of the Fujisaki-Okamoto transform—a method used to...

SQL-Injection-Detection-System

SQL Injection Detection System A comprehensive full-stack web...

Hiding the Trees in the Forest: Building Network Covert Channels with Hash-Based Covert Carrier Filtering

As an effective anti-censorship mechanism, network covert channels can provide data privacy protection and ensure communication security. However, the covertness of existing network covert channels primarily depends on the secrecy of their covert algorithms. With the increasing depth of research ...

Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems

The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels. The proliferation of email communication is apparent in both professional and personal contexts, thereby creating numerous vulnerabilitie...

The Chronicles of Radio Frequency Fingerprinting

Radio Frequency Fingerprinting RFF has evolved from an early idea for radar emitter identification into a broad research field for wireless device identification and spectrum monitoring for security. Rather than presenting a conventional literature survey, this work provides a critical historical...

Only 10% of SOCs Say They’re Getting Excellent Value From AI. Here’s What the Second Wave Has to Deliver

Eighteen months ago, the AI SOC was a marketing line. Today it's a budget item. The category has crossed over from interesting to inevitable, with billions of dollars now flowing into AI-powered security operations platforms, agentic SOC tools, and AI co-pilots built into every layer of the...

The Sound of Malware: A Memory Forensics Approach for Android Malware Analysis Via Audio Signals

Android malware analysis is currently facing increasing challenges in achieving robust classification and detecting stealth attacks. Modern threats employ advanced evasion strategies such as code obfuscation, dynamic loading, packing, and even steganographic manipulation of traditional static and...

FDM: A Framework for Decision-Making to Build ML-Based Malware Detection Systems

Selecting appropriate machine learning ML configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineering, and update mechanisms must jointly satisfy operational constraints that vary across deployment contexts. This paper proposes the Framework for...

CVE-2026-11033

CVE-2026-11033 affects Google Chrome on macOS due to an uninitialized use in WebML. The issue allows a remote attacker to potentially read sensitive data from process memory via a crafted HTML page. Affected software is Chrome on Mac, with the vulnerability described as occurring before Chrome ve...

Cognitive Threat Intelligence and Explainable Federated Security Analytics for Distributed Infrastructure Systems

The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things IoT technologies, and edge-based architectures has significantly expanded the cybersecurity attack surface and introduced increasingly sophisticated cyber threats. Conventional centralized intrusion...

Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities. AI-powered governance and automated decision-making systems are becoming a ke...

TinyML-Driven Cybersecurity for Autonomous Spacecraft: Latency-Accuracy Analysis for SPARTA RF and Cyber Threat Detection

Autonomous spacecraft require rapid, lightweight, and reliable onboard detection of cyber-RF threats. Using the SPARTA attack model, we analyze the latency-accuracy trade-offs of TinyML-compatible classical models -- Random Forest, Logistic Regression, SVM, and MLP -- for detecting uplink jamming...

AI Used to Decrypt Medieval Ciphers

Researchers are using machine learning algorithms to decrypt historical pencil-and-paper ciphers...