HackerOne: Residual Malicious Payloads on HackerOne after Vulnerability Fixes

A vulnerability was previously discovered on the HackerOne platform that allowed users to add malicious payloads to their profile pages. Despite remediation efforts, some of these malicious payloads were not fully removed from user profiles. This situation meant that the malicious content could...

MixBridge: Heterogeneous Image-To-Image Backdoor Attack through Mixture of Schrödinger Bridges

This paper focuses on implanting multiple heterogeneous backdoor triggers in bridge-based diffusion models designed for complex and arbitrary input distributions. Existing backdoor formulations mainly address single-attack scenarios and are limited to Gaussian noise input models. To fill this gap...

RADEP: a Resilient Adaptive Defense Framework against Model Extraction Attacks

Machine Learning as a Service MLaaS enables users to leverage powerful machine learning models through cloud-based APIs, offering scalability and ease of deployment. However, these services are vulnerable to model extraction attacks, where adversaries repeatedly query the application programming...

CVE-2022-45389

A missing permission check in Jenkins XP-Dev Plugin 1.0 and earlier allows unauthenticated attackers to trigger builds of jobs corresponding to an attacker-specified repository...

CVE-2020-7301

Cross Site scripting vulnerability in McAfee Data Loss Prevention DLP ePO extension prior to 11.5.3 allows authenticated attackers to trigger alerts via the file upload tab in the DLP case management section...

SUSE CVE-2025-37989

In the Linux kernel, the following vulnerability has been resolved: net: phy: leds: fix memory leak A network restart test on a router led to an out-of-memory condition, which was traced to a memory leak in the PHY LED trigger code. The root cause is misuse of the devm API. The registration...

CVE-2025-37989 net: phy: leds: fix memory leak

In the Linux kernel, the following vulnerability has been resolved: net: phy: leds: fix memory leak A network restart test on a router led to an out-of-memory condition, which was traced to a memory leak in the PHY LED trigger code. The root cause is misuse of the devm API. The registration...

ROSA: Finding Backdoors with Fuzzing

A code-level backdoor is a hidden access, programmed and concealed within the code of a program. For instance, hard-coded credentials planted in the code of a file server application would enable maliciously logging into all deployed instances of this application. Confirmed software supply chain...

Where the Devil Hides: Deepfake Detectors Can No Longer Be Trusted

With the advancement of AI generative techniques, Deepfake faces have become incredibly realistic and nearly indistinguishable to the human eye. To counter this, Deepfake detectors have been developed as reliable tools for assessing face authenticity. These detectors are typically developed on De...

Malicious code in matlab-cli (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 ff0a277d45885a5c1a26f027495e73b5e0aa8b49c7ee3eeafd06cc14e6e8f754 Importing the module starts a reverse shell --- Category: MALICIOUS - The campaign has clearly malicious intent, like infostealers. Campaign: 2025-05-matlab-cl...

Backdoor Attacks against Patch-Based Mixture of Experts

As Deep Neural Networks DNNs continue to require larger amounts of data and computational power, Mixture of Experts MoE models have become a popular choice to reduce computational complexity. This popularity increases the importance of considering the security of MoE architectures. Unfortunately,...

DEBIAN-CVE-2022-49798

In the Linux kernel, the following vulnerability has been resolved: tracing: Fix race where eprobes can be called before the event The flag that tells the event to call its triggers after reading the event is set for eprobes after the eprobe is enabled. This leads to a race where the eprobe may b...

CVE-2022-49798

CVE-2022-49798 describes a race in the Linux kernel tracing eprobes where the event’s triggers could be invoked before the event record is populated, risking a NULL dereference. This is caused by a flag set for eprobes after enabling the eprobe, potentially triggering at the start of the event wh...

PT-2025-18597 · Linux +1 · Linux Kernel +1

Name of the Vulnerable Software and Affected Versions: Linux kernel versions prior to the fixed version Description: The issue is related to the ext4 file system in the Linux kernel. It occurs when the ext4 da release space function is called with an inode that has inline data, leading to a warni...

Protocol-Agnostic and Data-Free Backdoor Attacks on Pre-Trained Models in RF Fingerprinting

While supervised deep neural networks DNNs have proven effective for device authentication via radio frequency RF fingerprinting, they are hindered by domain shift issues and the scarcity of labeled data. The success of large language models has led to increased interest in unsupervised pre-train...

How to Backdoor the Knowledge Distillation

Knowledge distillation has become a cornerstone in modern machine learning systems, celebrated for its ability to transfer knowledge from a large, complex teacher model to a more efficient student model. Traditionally, this process is regarded as secure, assuming the teacher model is clean. This...

SFIBA: Spatial-Based Full-Target Invisible Backdoor Attacks

Multi-target backdoor attacks pose significant security threats to deep neural networks, as they can preset multiple target classes through a single backdoor injection. This allows attackers to control the model to misclassify poisoned samples with triggers into any desired target class during...

FFCBA: Feature-Based Full-Target Clean-Label Backdoor Attacks

Backdoor attacks pose a significant threat to deep neural networks, as backdoored models would misclassify poisoned samples with specific triggers into target classes while maintaining normal performance on clean samples. Among these, multi-target backdoor attacks can simultaneously target multip...

BadMoE: Backdooring Mixture-Of-Experts LLMs Via Optimizing Routing Triggers and Infecting Dormant Experts

Mixture-of-Experts MoE have emerged as a powerful architecture for large language models LLMs, enabling efficient scaling of model capacity while maintaining manageable computational costs. The key advantage lies in their ability to route different tokens to different "expert'' networks within th...

AGATE: Stealthy Black-Box Watermarking for Multimodal Model Copyright Protection

Recent advancement in large-scale Artificial Intelligence AI models offering multimodal services have become foundational in AI systems, making them prime targets for model theft. Existing methods select Out-of-Distribution OoD data as backdoor watermarks and retrain the original model for...