Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis

Vulnerability detection in C/C++ software remains a major security challenge due to code complexity, manual memory management, and the limitations of traditional static analysis. Reinforcement Learning RL has emerged as a promising approach, particularly for fuzzing, test generation, program...

WPProbe Plugin Enumeration Tool 0.12.3

A fast WordPress plugin and theme scanner that detects installed plugins via REST API enumeration and themes from HTML discovery, then maps them to known vulnerabilities. Over 5,000 plugins detectable without brute-force, thousands more with it...

CyberChainBench: Can AI Agents Secure Smart Contracts against Real-World On-Chain Vulnerabilities?

We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability detection, exploit generation, and patch synthesis. Built from 541 real-world exploit incidents from DeFiHackLabs spanning 9 EVM chains, the benchmark...

The Rise of AI-Powered Academic Fraud: Beyond Traditional Plagiarism

AI has changed academic fraud. It now creates original-looking work, fake sources, and hidden misconduct that schools must learn to detect...

Duplicate Advisory: Picklescan has a missing detection when calling built-in python idlelib.autocomplete.AutoComplete.fetch_completions

Duplicate Advisory This advisory has been withdrawn because it is a duplicate of GHSA-7cq8-mj8x-j263. This link is maintained to preserve external references. Original Description picklescan before 0.0.29 fails to detect malicious pickle files using...

CVE-2025-71341

picklescan before 0.0.29 fails to detect the profile.Profile.runctx function when analyzing pickle files, allowing attackers to embed undetected malicious code. Remote attackers can craft malicious pickle files using profile.Profile.runctx in the reduce method to achieve remote code execution whe...

AI Threat Readiness Pillar 4: Detect and contain threats in real-time

Your guide to operationalizing AI-powered threat detection and response with Wiz to stay ahead of AI-driven attackers...

CVE-2025-71376

CVE-2025-71376 affects the Python package picklescan prior to 0.0.29. The vulnerability arises because idlelib.autocomplete.AutoComplete.fetch_completions in reduce methods is not detected, allowing attackers to embed code in pickle files that executes arbitrary commands when loaded by victims. T...

CVE-2025-71370

Vulnerability summary (CVE-2025-71370): picklescan before 0.0.28 fails to detect malicious torch.jit.unsupported_tensor_ops.execWrapper function calls embedded in pickle files. Attackers can craft malicious pickle files that bypass picklescan detection and execute arbitrary code when loaded via p...

CVE-2025-71365

The CVE affects picklescan (before 0.0.33) where the detector fails to catch malicious pickle payloads that invoke numpy.f2py.crackfortran.myeval via the reduce method, allowing arbitrary code execution when loaded. Root cause: detection bypass in pickle loading path. Impact: remote code executio...

EUVD-2025-210305

picklescan before 0.0.29 fails to detect the profile.Profile.runctx function when analyzing pickle files, allowing attackers to embed undetected malicious code. Remote attackers can craft malicious pickle files using profile.Profile.runctx in the reduce method to achieve remote code execution whe...

CVE-2025-71365

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded...

CVE-2025-71365 picklescan - Arbitrary Code Execution via numpy.f2py.crackfortran.myeval Detection Bypass

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded...

CVE-2025-71365 picklescan - Arbitrary Code Execution via numpy.f2py.crackfortran.myeval Detection Bypass

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded...

CVE-2025-71365 picklescan - Arbitrary Code Execution via numpy.f2py.crackfortran.myeval Detection Bypass

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded...

The Evolution of iGaming Fraud: What Security Teams Should Expect in 2027

Learn how AI, deepfakes, synthetic identities and fraud-as-a-service may reshape iGaming risk, and what security teams can do to detect future threats in 2027...

sharepoint-sqli-research

SharePoint SQL Injection Research Analysis of SQL injection v...

podman security update

5.8.2-3.0.1 - Rework CNI/Netavark detection logic JIRA: EVG-3769 - Rebuild on new golang to support experimental GODEBUG fipsnoenforceems - Drop nmap-ncat requirement and skip ignore-socket test case Orabug: 34117404 6:5.8.2-3 - Rebuild for CVE-2026-32283 - Resolves: RHEL-167685 6:5.8.2-2 - Rebui...

A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids

The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface for sophisticated cyberattacks, FDI attacks that stealthily distort state estimation and DoS/DDoS attacks that flood communication channels...

Poster: Exploring the Limits of Audio-Based Detection of Turkish Phone Call Scams

Scam phone calls exploit vulnerable communities worldwide, yet research on detection has focused almost exclusively on English and other high-resource languages. In low-resource settings such as Turkish, detection is especially difficult, as annotated data is scarce and technological defenses...