FecalFace

설명 | 설치 | 웹사이트 | 이슈 FecalFace 설명 Fecal face는 "Shit Bucket" 개념에서 탄생했습니다. 아이디어는 서로에 대해 수집된 데이터를 썩히는rot 것입니다. 구체적으로는 얼굴 감지 및 인식과 관련됩니다. 이 프로젝트의 동기는 우리의 얼굴을 수집하는 회사들이 더 이상 우리를 식별할 수 없도록 하는 것입니다. 이 프로젝트현재는 PoC는 다음을 포함합니다: 입력된 RRSS 아바타에서 얼굴 감지 자신의 계정으로 식별 보호를 위한 적대적 입력adversarial inputs 공격 사용 식별 없이 감지 ...

Defenses-for-Tool-Integrated-LLM

ツール統合型LLMエージェントに対する敵対的攻撃への汎用防御 このリポジトリには、ツール統合型大規模言語モデル（LLM）エージェントを敵対的攻撃から防御するための私たちのプロジェクトのコードと実験が含まれています。 概要 私たちはAgent Security Bench（ASB）を基盤として、ツールと構造化推論（例：chain-of-thought、reflection）の統合が、複数のタスクシナリオにわたる敵対的プロンプトに対するLLMエージェントの脆弱性にどのように影響するかを評価します。 このリポジトリには以下が含まれます：...

attack-attention

ファウンデーションモデルの注意機構への攻撃は下流タスクを効果的に破壊する CVPR 2025 の Adversarial Machine Learning on Computer Vision: Foundation Models + X ADVML ワークショップで採択された論文 "ファウンデーションモデルの注意機構への攻撃は下流タスクを効果的に破壊する" の公式 PyTorch 実装です。 ファウンデーションモデルの注意機構への攻撃は下流タスクを効果的に破壊する" Hondamunige Prasanna Silva, Federico Becattini and Lorenzo...

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...

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...

A Practical Adversarial Attack against Sequence-Based Deep Learning Malware Classifiers

Sequence-based deep learning models e.g., RNNs, can detect malware by analyzing its behavioral sequences. Meanwhile, these models are susceptible to adversarial attacks. Attackers can create adversarial samples that alter the sequence characteristics of behavior sequences to deceive malware...

Between a Rock and a Hard Place: Exploiting Ethical Reasoning to Jailbreak LLMs

Large language models LLMs have undergone safety alignment efforts to mitigate harmful outputs. However, as LLMs become more sophisticated in reasoning, their intelligence may introduce new security risks. While traditional jailbreak attacks relied on singlestep attacks, multi-turn jailbreak...

ZIUM: Zero-Shot Intent-Aware Adversarial Attack on Unlearned Models

Machine unlearning MU removes specific data points or concepts from deep learning models to enhance privacy and prevent sensitive content generation. Adversarial prompts can exploit unlearned models to generate content containing removed concepts, posing a significant security risk. However,...

Radio Adversarial Attacks on EMG-Based Gesture Recognition Networks

Surface electromyography EMG enables non-invasive human-computer interaction in rehabilitation, prosthetics, and virtual reality. While deep learning models achieve over 97% classification accuracy, their vulnerability to adversarial attacks remains largely unexplored in the physical domain. We...

Generating Adversarial Point Clouds Using Diffusion Model

Adversarial attack methods for 3D point cloud classification reveal the vulnerabilities of point cloud recognition models. This vulnerability could lead to safety risks in critical applications that use deep learning models, such as autonomous vehicles. To uncover the deficiencies of these models...

Scaling Decentralized Learning with FLock

Fine-tuning the large language models LLMs are prevented by the deficiency of centralized control and the massive computing and communication overhead on the decentralized schemes. While the typical standard federated learning FL supports data privacy, the central server requirement creates a...