474 matches found
EUVD-2018-4127
Malware in sbrugna...
EUVD-2020-24072
Malware in sbrugna...
EUVD-2014-4117
Malware in sbrugna...
EUVD-2011-1380
Malware in sbrugna...
P2P: A Poison-To-Poison Remedy for Reliable Backdoor Defense in LLMs
During fine-tuning, large language models LLMs are increasingly vulnerable to data-poisoning backdoor attacks, which compromise their reliability and trustworthiness. However, existing defense strategies suffer from limited generalization: they only work on specific attack types or task settings...
EUVD-2023-58694
Malicious code in bioql PyPI...
EUVD-2024-46974
Malicious code in bioql PyPI...
EUVD-2025-28960
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...
Backdoor Attacks against Speech Language Models
Large Language Models LLMs and their multimodal extensions are becoming increasingly popular. One common approach to enable multimodality is to cascade domain-specific encoders with an LLM, making the resulting model inherit vulnerabilities from all of its components. In this work, we present the...
SecureFixAgent: a Hybrid LLM Agent for Automated Python Static Vulnerability Repair
Modern software development pipelines face growing challenges in securing large codebases with extensive dependencies. Static analysis tools like Bandit are effective at vulnerability detection but suffer from high false positives and lack repair capabilities. Large Language Models LLMs, in...
Beyond Surface Alignment: Rebuilding LLMs Safety Mechanism Via Probabilistically Ablating Refusal Direction
Jailbreak attacks pose persistent threats to large language models LLMs. Current safety alignment methods have attempted to address these issues, but they experience two significant limitations: insufficient safety alignment depth and unrobust internal defense mechanisms. These limitations make...
A Systematic Evaluation of Parameter-Efficient Fine-Tuning Methods for the Security of Code LLMs
Code-generating Large Language Models LLMs significantly accelerate software development. However, their frequent generation of insecure code presents serious risks. We present a comprehensive evaluation of seven parameter-efficient fine-tuning PEFT techniques, demonstrating substantial gains in...
Early Approaches to Adversarial Fine-Tuning for Prompt Injection Defense: a 2022 Study of GPT-3 and Contemporary Models
This paper documents early research conducted in 2022 on defending against prompt injection attacks in large language models, providing historical context for the evolution of this critical security domain. This research focuses on two adversarial attacks against Large Language Models LLMs: promp...
ALSA: hda/ca0132: Fix buffer overflow in add_tuning_control
...
CVE-2025-39751
Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority...
CVE-2025-39751
Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority...
DEBIAN-CVE-2025-39751
In the Linux kernel, the following vulnerability has been resolved: ALSA: hda/ca0132: Fix buffer overflow in addtuningcontrol The 'sprintf' call in 'addtuningcontrol' may exceed the 44-byte buffer if either string argument is too long. This triggers a compiler warning. Replaced 'sprintf' with...
