ODPure

ODPure: Backdoor Purification for Object Detection via Ensemble Corruption Consensus The first input-stage black-box purification defense tailored for object detectors against backdoor attacks. 📖 Overview ODPure is the first input-stage, black-box purification framework tailored for defending...

SynGhost

Syntactic-Ghost SynGhost SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer Contributions & Characteristics SynGhost has the following contributions To mitigate the risks of existing task-agnostic backdoors, we propose $\mathttmaxEntropy$, an entropy-based...

Backdoor-Attack-Defense-LLMs

Interpretability-of-LLMs The IBSD is the code of the published paper "IBSD: Iterable Black-box Self-defense Against Backdoor Attacks" on the IEEE Signal Processing Letters 2025.10paper link The SLIP is the code of the published paper "SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based...

Information Blackhole: Exploring Backdoor Mechanism in 3D Point Cloud Reconstruction

Point cloud autoencoders are fundamental components for 3D world representation and support many safety-critical downstream applications. Existing studies have extensively investigated backdoor attacks on point cloud classification, whereas backdoor attacks against point cloud autoencoders remain...

ODPure: Backdoor Purification for Object Detection Via Ensemble Corruption Consensus

With the development of applications like autonomous driving, object detection has gained significant attention, while also highlighting critical vulnerabilities like backdoor attacks that severely compromise model integrity. Specifically, such attacks involve altering the categories of objects...

RAMP: Reversing Adversarial Perturbations to Strengthen Clean-Label Backdoor Attacks against Malware Detectors

Deep learning-based malware detectors are commonly updated by fine-tuning on newly collected samples, but this practical update pipeline also creates an attack surface for training-time backdoor attacks. In realistic crowdsourced data collection, however, strict label vetting typically restricts...

Empirical Evaluation of Data Poisoning Attacks in Supervised Learning

Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-time attacks, label flipping and backdoor poisoning, on MNIST and Fashion-MNIST with three baseline classifiers: Logistic Regression, Linear SVM, and...

TrojanWorld: Backdooring World-Model Agents Via Imagination Steering

World models increasingly serve as the predictive core of model-based reinforcement learning agents, enabling them to simulate future dynamics and reason over imagined trajectories before acting. Their substantial training demands make pretrained world models attractive for distribution and reuse...

AKRASIA: Stealthy Backdoor Attack on Reasoning-Based Code LLMs

We present AKRASIA, a stealthy, inference-time backdoor attack against reasoning-based Code LLMs. AKRASIA aims to achieve a backdoor target e.g., malicious code execution in reasoning LLMs while evading automated defenses and human inspection. To achieve this, AKRASIA probes the victim LLM to...

Low-ASR Backdoors: Exploiting Attack Success Rate Reduction and Attacker-Defender Asymmetry

Backdoor attacks are among the most effective and stealthy attacks in deep learning. Existing attacks and defenses are largely designed and evaluated under the assumption that successful backdoors exhibit high Attack Success Rates ASRs. In this paper, we show that this assumption creates a...

Temporal Poisoning: Clean-Label Backdoors Via Event Redistribution in SNNs

Backdoor attacks on Spiking Neural Networks SNNs have primarily assumed dirty-label poisoning, in which triggered training samples are relabeled to an attacker-selected class. We study clean-label temporal poisoning, where a fixed timestamp transformation is applied only to the target-class...

Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel

Semantic communication SemCom aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless channels. The adoption of SemCom in shared-access wireless networks introduces new vulnerabilities for multi-user semantic inference. This paper conside...

A Hybrid Intrusion Detection System for Electric Vehicle Charging Infrastructure

The integration of Electric Vehicle Charging Stations EVCSs into the smart grid necessitates sophisticated digital infrastructure for their management and coordination, which expands the attack surface and makes both the power grid and EVCSs vulnerable to cyberattacks. This research addresses...

Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning

Prior research suggests that differential privacy DP inherently enhances the robustness of federated learning FL against backdoor attacks. In this paper, we challenge this assumption. Through an empirical analysis of two baseline attack strategies, we uncover a fundamental tension in DP-FL: while...

Model Poisoning against Federated Model Adaptation with Chain of Bit-Flips

Federated Learning FL allows a set of clients to collectively train a global model without sharing local training data. Giving the responsibility of the training to decentralized actors may lead to poisoning attacks: clients controlled by malicious third party potentially poison the training...

BadBone: Backdoor Attacks against Backbone Models in Visual Prompt Learning

Prompt learning is a new machine learning paradigm that has attracted ample attention due to its simplicity and proven efficacy. Despite its growing adoption, the security vulnerabilities associated with this paradigm remain underexplored. In this work, we take the first step to propose BadBone, ...

Backdooring Masked Diffusion Language Models

Masked diffusion language models MDLMs are emerging as a compelling new paradigm for text generation, but their training-time security remains largely unexplored. Existing backdoor attacks on Gaussian diffusion models or autoregressive language models do not directly apply to MDLMs because MDLMs...

MetaBackdoor: Exploiting Positional Encoding As a Backdoor Attack Surface in LLMs

Backdoor attacks pose a serious security threat to large language models LLMs, which are increasingly deployed as general-purpose assistants in safety- and privacy-critical applications. Existing LLM backdoors rely primarily on content-based triggers, requiring explicit modification of the input...

DETOUR: A Practical Backdoor Attack against Object Detection

Object detection OD is critical to real-world vision systems, yet existing backdoor attacks on detection transformers DETRs for OD tasks rely on patch-wise triggers optimized at fixed locations with minimal perturbations. Such attacks overlook that backdoor triggers in the real world may appear a...

SlowBA: An Efficiency Backdoor Attack Towards VLM-Based GUI Agents

Modern vision-language-model VLM based graphical user interface GUI agents are expected not only to execute actions accurately but also to respond to user instructions with low latency. While existing research on GUI-agent security mainly focuses on manipulating action correctness, the security...