EUVD-2022-55191

Malicious code in bioql PyPI...

EUVD-2025-9381

Malicious code in bioql PyPI...

EUVD-2022-0947

Malicious code in bioql PyPI...

EUVD-2023-33769

Malicious code in bioql PyPI...

EUVD-2022-41061

Malicious code in bioql PyPI...

EUVD-2022-55264

Malicious code in bioql PyPI...

EUVD-2023-30090

Malicious code in bioql PyPI...

EUVD-2022-55489

Malicious code in bioql PyPI...

EUVD-2023-1861

Malicious code in bioql PyPI...

EUVD-2022-37439

Malicious code in bioql PyPI...

EUVD-2023-44402

Malicious code in bioql PyPI...

EUVD-2021-34671

Malicious code in bioql PyPI...

EUVD-2025-14216

Malicious code in bioql PyPI...

MALF: A Multi-Agent LLM Framework for Intelligent Fuzzing of Industrial Control Protocols

Industrial control systems ICS are vital to modern infrastructure but increasingly vulnerable to cybersecurity threats, particularly through weaknesses in their communication protocols. This paper presents MALF Multi-Agent LLM Fuzzing Framework, an advanced fuzzing solution that integrates large...

FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AI

Fuzz testing has become a cornerstone technique for identifying software bugs and security vulnerabilities, with broad adoption in both industry and open-source communities. Directly fuzzing a function requires fuzz drivers, which translate random fuzzer inputs into valid arguments for the target...

HFuzzer: Testing Large Language Models for Package Hallucinations Via Phrase-Based Fuzzing

Large Language Models LLMs are widely used for code generation, but they face critical security risks when applied to practical production due to package hallucinations, in which LLMs recommend non-existent packages. These hallucinations can be exploited in software supply chain attacks, where...

Red Teaming Quantum-Resistant Cryptographic Standards: A Penetration Testing Framework Integrating AI and Quantum Security

This study presents a structured approach to evaluating vulnerabilities within quantum cryptographic protocols, focusing on the BB84 quantum key distribution method and National Institute of Standards and Technology NIST approved quantum-resistant algorithms. By integrating AI-driven red teaming,...

Intelligent Graybox Fuzzing Via ATPG-Guided Seed Generation and Submodule Analysis

Hardware Fuzzing emerged as one of the crucial techniques for finding security flaws in modern hardware designs by testing a wide range of input scenarios. One of the main challenges is creating high-quality input seeds that maximize coverage and speed up verification. Coverage-Guided Fuzzing CGF...

Semantic-Aware Fuzzing: an Empirical Framework for LLM-Guided, Reasoning-Driven Input Mutation

Security vulnerabilities in Internet-of-Things devices, mobile platforms, and autonomous systems remain critical. Traditional mutation-based fuzzers -- while effectively explore code paths -- primarily perform byte- or bit-level edits without semantic reasoning. Coverage-guided tools such as AFL+...

Exploit for Heap-based Buffer Overflow in Google Android

This is a PoC exploit for CVE-2020-8899, a memory corruption vulnerability in the Samsung Qmage codec. The exploit targets a Samsung Galaxy Note 10+ phone running Android 10 via MMS. The exploit code is written in Python and requires the following software to be locally installed: Python 3, Netwi...