EUVD-2021-9075

Malicious code in bioql PyPI...

EUVD-2021-9073

Malicious code in bioql PyPI...

EUVD-2021-9072

Malicious code in bioql PyPI...

EUVD-2021-9076

Malicious code in bioql PyPI...

EUVD-2021-9078

Malicious code in bioql PyPI...

EUVD-2021-9074

Malicious code in bioql PyPI...

EUVD-2021-9077

Malicious code in bioql PyPI...

Adversarial Bug Reports As a Security Risk in Language Model-Based Automated Program Repair

Large Language Model LLM - based Automated Program Repair APR systems are increasingly integrated into modern software development workflows, offering automated patches in response to natural language bug reports. However, this reliance on untrusted user input introduces a novel and underexplored...

The WASM Cloak: Evaluating Browser Fingerprinting Defenses under WebAssembly Based Obfuscation

Browser fingerprinting defenses have historically focused on detecting JavaScriptJS-based tracking techniques. However, the widespread adoption of WebAssembly WASM introduces a potential blind spot, as adversaries can convert JS to WASM's low-level binary format to obfuscate malicious logic. This...

Benchmarking Fraud Detectors on Private Graph Data

We introduce the novel problem of benchmarking fraud detectors on private graph-structured data. Currently, many types of fraud are managed in part by automated detection algorithms that operate over graphs. We consider the scenario where a data holder wishes to outsource development of fraud...

CVE-2025-47819

Flock Safety Gunshot Detection devices before 1.3 have an on-chip debug interface with improper access control...

GradEscape: a Gradient-Based Evader against AI-Generated Text Detectors

In this paper, we introduce GradEscape, the first gradient-based evader designed to attack AI-generated text AIGT detectors. GradEscape overcomes the undifferentiable computation problem, caused by the discrete nature of text, by introducing a novel approach to construct weighted embeddings for t...

3D Gaussian Splat Vulnerabilities

With 3D Gaussian Splatting 3DGS being increasingly used in safety-critical applications, how can an adversary manipulate the scene to cause harm? We introduce CLOAK, the first attack that leverages view-dependent Gaussian appearances - colors and textures that change with viewing angle - to embed...

CVE-2021-21905

Stack-based buffer overflow vulnerability exists in how the CMA readfile function of Garrett Metal Detectors iC Module CMA Version 5.0 is used at various locations. The Garrett iC Module exposes an authenticated CLI over TCP port 6877. This interface is used by a secondary GUI client, called “CMA...

CVE-2021-21902

An authentication bypass vulnerability exists in the CMA runserver6877 functionality of Garrett Metal Detectors iC Module CMA Version 5.0. A properly-timed network connection can lead to authentication bypass via session hijacking. An attacker can send a sequence of requests to trigger this...

CVE-2021-21901

A stack-based buffer overflow vulnerability exists in the CMA checkudpcrc function of Garrett Metal Detectors’ iC Module CMA Version 5.0. A specially-crafted packet can lead to a stack-based buffer overflow during a call to memcpy. An attacker can send a malicious packet to trigger this...

CVE-2021-21907

A directory traversal vulnerability exists in the CMA CLI getenv command functionality of Garrett Metal Detectors’ iC Module CMA Version 5.0. A specially-crafted command line argument can lead to local file inclusion. An attacker can provide malicious input to trigger this vulnerability...

CVE-2021-21903

A stack-based buffer overflow vulnerability exists in the CMA checkudpcrc function of Garrett Metal Detectors’ iC Module CMA Version 5.0. A specially-crafted packet can lead to a stack-based buffer overflow during a call to strcpy. An attacker can send a malicious packet to trigger this...

CVE-2021-21904

A directory traversal vulnerability exists in the CMA CLI setenv command of Garrett Metal Detectors’ iC Module CMA Version 5.0. An attacker can provide malicious input to trigger this vulnerability...

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...