EUVD-2025-15889

Malicious code in bioql PyPI...

EUVD-2025-15903

Malicious code in bioql PyPI...

EUVD-2022-30842

Malicious code in bioql PyPI...

EUVD-2022-40629

Malicious code in bioql PyPI...

EUVD-2022-37679

Malicious code in bioql PyPI...

EUVD-2023-36572

Malicious code in bioql PyPI...

EUVD-2025-16644

Malicious code in bioql PyPI...

EUVD-2025-15301

Malicious code in bioql PyPI...

EUVD-2025-16082

Malicious code in bioql PyPI...

EUVD-2022-24814

Malicious code in bioql PyPI...

EUVD-2025-16496

Malicious code in bioql PyPI...

EUVD-2025-16497

Malicious code in bioql PyPI...

EUVD-2025-16478

Malicious code in bioql PyPI...

EUVD-2025-16469

Malicious code in bioql PyPI...

EUVD-2025-15273

Malicious code in bioql PyPI...

EUVD-2025-16502

Malicious code in bioql PyPI...

EUVD-2025-16500

Malicious code in bioql PyPI...

EUVD-2021-7031

Malicious code in bioql PyPI...

Vulnerability in the Oracle Java SE, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: Sound). Supported versions that are affected are Oracle Java SE: 8u351, 8u351-perf, 11.0.17, 17.0.5, 19.0.1; Oracle GraalVM Enterprise Edition: 20.3.8, 21.3.4 and 22.3.0. Difficult to exploit vulnerability allows unauthenticated attacker with network access via multiple protocols to compromise Oracle Java SE, Oracle GraalVM Enterprise Edition. Successful attacks of this vulnerability can result in unauthorized update, insert or delete access to some of Oracle Java SE, Oracle GraalVM Enterprise Edition accessible data. Note: This vulnerability applies to Java deployments, typically in clients running sandboxed Java Web Start applications or sandboxed Java applets, that load and run untrusted code (e.g., code that comes from the internet) and rely on the Java sandbox for security. This vulnerability does not apply to Java deployments, typically in servers, that load and run on

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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...