FecalFace

Description | Installation | Website | Issues FecalFace Description Fecal face was born from the concept of "Shit Bucket" where the idea is to try to rot the data they have about each other. Specifically, it is related to the detection and recognition of faces. The motivation of this project is...

attack-attention

Attacking Attention of Foundation Models Effectively Disrupts Downstream Tasks Official PyTorch implementation of the paper "Attacking Attention of Foundation Models Effectively Disrupts Downstream Tasks" , accepted in the Adversarial Machine Learning on Computer Vision: Foundation Models + X ADV...

Defenses-for-Tool-Integrated-LLM

Universal Defences for Tool-Integrated LLM Agents Against Adversarial Attacks This repository contains the code and experiments for our project on defending tool-integrated large language model LLM agents against adversarial attacks. Overview We build upon Agent Security Bench ASB to evaluate how...

Exploiting Vulnerabilities: Universal Adversarial Attacks on Vision-Language-Action Models in Robotics

Recently, Vision-Language-Action VLA models have revolutionized robotic manipulation by seamlessly integrating visual perception, language understanding, and action generation in an end-to-end learning framework. However, since these models are designed to interact directly with the physical worl...

Adversarial Testing of Automated Program Repair Agents for Security Vulnerabilities

Software agents with Large Language Models LLMs are designed for Automated Program Repair APR tasks, raising the possibility that, in the near future, APR agents will fix bugs automatically without much human intervention. Can we trust an APR agent to produce both functionally correct and secure...

PhantomCall: Evading ML Malware Detectors Via Function Call Graph Perturbation

Prior adversarial attacks on Windows PE malware detectors target raw bytes, PE headers, or intra-function control-flow graphs, leaving the function call graph FCG unexplored as an attack surface. Yet the FCG structure is an important feature in graph-based malware detectors. We present Phan-...

Vulnerable Code Search: Transferable Attack for Code Language Models

Reliable code retrieval is crucial for developer productivity and effective code reuse. However, current neural code language models CLMs powering search tools are susceptible to adversarial attacks targeting non-functional textual elements. In this paper, we introduce a programming...

DSPrompt: Dynamic Soft Prompt Defense against M-RAG Corruption

Multimodal Retrieval Augmented Generation M-RAG is increasingly vulnerable to adversarial attacks where malicious data are crafted to produce embeddings that align with benign entries in the vector space, deceiving retrieval and inducing harmful outputs. Existing defenses primarily operate at que...

Breaking and Defending LLM-Powered Social Media Bot Detection Systems

The rise of social media bots poses a persistent threat, enabling misinformation, opinion manipulation, and the erosion of trust in online platforms. To combat this, machine learning systems have been developed to detect and limit bot activity, but attackers continuously adapt through techniques...

Generating Attacks for LLMs with GFlowNets

The rapid advancement of Large Language Models LLMs has facilitated their ubiquitous integration into various domains, leading to widespread adoption. However, this escalating trend has introduced significant security vulnerabilities, necessitating the identification and mitigation of flaws arisi...

Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs

Transformer-based malware detection systems operating on graph modalities such as control flow graphs CFGs achieve strong performance by modeling structural relationships in program behavior. However, their robustness to adversarial evasion attacks remains underexplored. This paper examines the...

Recovery-Induced Erasure Attack on QKD Systems

Detector dead time is typically treated as a fixed parameter in quantum key distribution QKD security analyses. In practice, however, the effective recovery time of single-photon avalanche photodiodes SPADs depends on the incident count rate. In this work, we demonstrate that this...

The Role of Learning in Attacking Intrusion Detection Systems

Recent work on network attacks have demonstrated that ML-based network intrusion detection systems NIDS can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. ...

Breaking Audio Large Language Models by Attacking Only the Encoder: A Universal Targeted Latent-Space Audio Attack

Audio-language models combine audio encoders with large language models to enable multimodal reasoning, but they also introduce new security vulnerabilities. We propose a universal targeted latent space attack, an encoder-level adversarial attack that manipulates audio latent representations to...

LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors

The rapid growth in both the scale and complexity of Android malware has driven the widespread adoption of machine learning ML techniques for scalable and accurate malware detection. Despite their effectiveness, these models remain vulnerable to adversarial attacks that introduce carefully crafte...

IoT-Based Android Malware Detection Using Graph Neural Network with Adversarial Defense

Since the Internet of Things IoT is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from applications as graphs to generate graph embeddings...

A Novel and Practical Universal Adversarial Perturbations against Deep Reinforcement Learning Based Intrusion Detection Systems

Intrusion Detection Systems IDS play a vital role in defending modern cyber physical systems against increasingly sophisticated cyber threats. Deep Reinforcement Learning-based IDS, have shown promise due to their adaptive and generalization capabilities. However, recent studies reveal their...

GRAPHTEXTACK: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs

Text-attributed graphs TAGs, which combine structural and textual node information, are ubiquitous across many domains. Recent work integrates Large Language Models LLMs with Graph Neural Networks GNNs to jointly model semantics and structure, resulting in more general and expressive models that...

Colliding with Adversaries at ECML-PKDD 2025 Adversarial Attack Competition 1st Prize Solution

This report presents the winning solution for Task 1 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery at ECML-PKDD 2025. The task required designing an adversarial attack against a provided classification model that maximizes misclassification while...

NatGVD: Natural Adversarial Example Attack Towards Graph-Based Vulnerability Detection

Graph-based models learn rich code graph structural information and present superior performance on various code analysis tasks. However, the robustness of these models against adversarial example attacks in the context of vulnerability detection remains an open question. This paper proposes...