35639 matches found
Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems
Coordinated stealth attacks are a serious cybersecurity threat to distributed generation systems because they modify control and measurement signals while remaining close to normal behavior, making them difficult to detect using standard intrusion detection methods. This study investigates quantu...
Picklescan is vulnerable to RCE via missing detection when calling numpy.f2py.crackfortran.param_eval
Summary Picklescan uses numpy.f2py.crackfortran.parameval, which is a function in numpy to execute remote pickle files. Details The attack payload executes in the following steps: - First, the attacker crafts the payload by calling the numpy.f2py.crackfortran.parameval function via reduce method....
Exploit for CVE-2025-14847
🌊 MongoDeepDive Context-Aware MongoDB Wire Protocol Explo...
Picklescan is vulnerable to RCE through missing detection when calling built-in python operator.methodcaller
Summary Picklescan uses operator.methodcaller, which is a built-in python library function to execute remote pickle files. Details The attack payload executes in the following steps: - First, the attacker crafts the payload by calling the operator.methodcaller function in method reduce. - Then,...
EUVD-2025-205639
Picklescan is vulnerable to RCE through missing detection when calling built-in python operator.methodcaller...
GHSA-X843-G5MX-G377 Picklescan is vulnerable to RCE through missing detection when calling built-in python operator.methodcaller
Summary Picklescan uses operator.methodcaller, which is a built-in python library function to execute remote pickle files. Details The attack payload executes in the following steps: - First, the attacker crafts the payload by calling the operator.methodcaller function in method reduce. - Then,...
EUVD-2025-205587
Picklescan missing detection when calling numpy.f2py.crackfortran.getlincoef...
EUVD-2025-205589
Picklescan missing detection when calling pty.spawn...
Exploit for CVE-2025-14847
CYBERDUDEBIVASH MONGODB DETECTOR TOOL v2026.1 Detect expose...
SQLite-Injection-Lab
آزمایشگاه تزریق SQL SQL Injection Lab یک محیط آموزشی جامع ب...
MeLeMaD: Adaptive Malware Detection Via Chunk-Wise Feature Selection and Meta-Learning
Confronting the substantial challenges of malware detection in cybersecurity necessitates solutions that are both robust and adaptable to the ever-evolving threat environment. The paper introduces Meta Learning Malware Detection MeLeMaD, a novel framework leveraging the adaptability and...
Zero-Trust Agentic Federated Learning for Secure IIoT Defense Systems
Recent attacks on critical infrastructure, including the 2021 Oldsmar water treatment breach and 2023 Danish energy sector compromises, highlight urgent security gaps in Industrial IoT IIoT deployments. While Federated Learning FL enables privacy-preserving collaborative intrusion detection,...
VIPSQLi
🔥 VIP SQLi Scanner - Professional Triage Tool REAL SQLi PEH...
Breaking the Illusion: Automated Reasoning of GDPR Consent Violations
Recent privacy regulations such as the General Data Protection Regulation GDPR and the California Consumer Privacy Act CCPA have established legal requirements for obtaining user consent regarding the collection, use, and sharing of personal data. These regulations emphasize that consent must be...
EUVD-2025-205479
Malicious code in ing-feat-malware-detection npm...
MAL-2025-192949 Malicious code in ing-feat-malware-detection (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 5339c05fd7459125a98a0503401e5eb8bca30c456d9a80f14485484df6256850 The package ing-feat-malware-detection was found to contain malicious code. Source: ghsa-malware...
Exploit for Improper Verification of Cryptographic Signature in Fortinet Fortiproxy
CVEs: CVE-2025-59718 / CVE-2025-59719 Fortinet Poc Herramient...
Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection Via Latent Space Representation Learning and Alignment
Although AI-based models have achieved high accuracy in IoT threat detection, their deployment in enterprise environments is constrained by reliance on stationary datasets that fail to reflect the dynamic nature of real-world IoT NetFlow traffic, which is frequently affected by concept drift...