PT-2026-54864

Name of the Vulnerable Software and Affected Versions Elasticsearch affected versions not specified Description An issue exists where the allocation of resources lacks limits or throttling. A user with elevated privileges can submit a specially crafted machine learning request that triggers...

Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence

Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements existing machine learning pipelines do not satisfy, since they treat original evidence as training data and produce opaque classifications without...

CVE-2026-56149: Allocation of Resources Without Limits or Throttling

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...

Comparative Analysis of Machine Learning Based Intrusion Detection in Realistic IoT Networks

The Internet of Things IoT is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more. Despite the benefits of using IoT devices, they present several challenges. Given the significant role these devices play in our lives, it is crucial to...

ShieldPatch

🛡️ ShieldPatch Predict . Protect . Prevail AI-driven vuln...

A Hybrid Framework for Crypto-Ransomware Detection in Enterprise Shared Storage

Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints. Consequently, ransomware operators have evolved variants that expand their attack surface from local systems to network drives and shared storage resources. ...

IITR_Capstone_RedScope_Project

RedScope Capstone Project Lab-only red-team assessment for we...

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...

UBUNTU-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...

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...

Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution

The growing reliance on pre-trained Machine Learning ML models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses. Current model-scanning solutions primarily rely on static,...

Scalable Malware Family Classification Using Quantum Kernel Based Machine Learning

The classification of malware families is a key challenge in cybersecurity, which enables threat attribution, analysis of attack operations, and the formulation of effective defense strategies. Emerging malware samples are becoming increasingly structurally similar and obfuscated, making accurate...

SQL-Injection-Detection-System

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

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...

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 ...

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...

The vulnerability of the ML lifecycle management platform, related to the shortcomings in the authentication process, allows a perpetrator to execute arbitrary code.

The vulnerability of the ML model lifecycle management platform is related to deficiencies in the authentication process. Exploiting this vulnerability allows a malicious actor to execute arbitrary code remotely...

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...