Modelgirls gets backdoor attacked
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Backdoor attacks on AI models pose a significant threat to cybersecurity. Discover the dangers, examples, and defensive strategies against these hidden threats in our latest blog.
Aug 5, 2025
Among these hidden functionalities, backdoor attack is a serious threat to DNN models [5], [6], [9]. In a backdoor attack, the attacker aims to inject a specific neuron activation pattern into a DNN model to manipulate its behaviors maliciously.
Nov 10, 2025
Jan 3, 2020
However, recent studies reveal that transformers are susceptible to backdoor attacks. Prior backdoor attack methods typically rely on retraining with clean data or altering the model architecture, both of which can be resource-intensive and intrusive.
Developing effective defenses against backdoor attacks in RL is crucial to ensure the security and trustworthiness of RL-based systems. Backdoor attacks can target RL agents used in critical applications such as autonomous driving [2], [3], resource scheduling [4], robotic control [5], and competitive games [3].
In this work, we focus on backdoor attacks against DNN models on image classification tasks, which are among the most successful ML applications deployed in the real world. In the backdoor attack setting, an adversary trains an ML model which can intentionally misclassify any input with an added trigger (a secret pattern constructed from a set of neighboring pixels, e.g., a white square) to a ...
Experiments on popu-lar benchmarks demonstrate a high success rate of MTAttack for multi-target attacks, substantially outperforming existing attack methods. Furthermore, our attack exhibits strong gen-eralizability across datasets and robustness against backdoor defense strategies.
A backdoor attack lets an attacker bypass normal security measures and access a computer system. Learn how they work and how to prevent them.
Oct 29, 2025
First row: Correct task execution. Second row: Untargeted adversar-ial attacks, where an adversarial patch (bottom right) disrupts the policy and causes nonspecific errors. Third row: Untargeted back-door attacks, where inserting a visual trigger (the basketball) sim-Fourth row: ilarly results in task failure and irrelevant behaviors.
A backdoor attack is a clandestine method of sidestepping normal authentication procedures to gain unauthorized access to a system.
Apr 1, 2025
Three-level Evaluation As the first targeted backdoor attack against VLA, to enable comprehensive analysis of attack performance, we designed a three-level evolution based on the level of goal completion. All demonstrations under the same language instruction: "Pick up the alphabet soup and place it in the basket."
Jun 18, 2025
Florida authorities released surveillance videos that showed a 24-year-old woman who fought off an attacker inside her apartment building's gym. NBC's Prisci...
An effective backdoor attack on LoRA adapters must satisfy two key requirements, as in standard backdoor attacks: (1) successfully implanting malicious behavior, and (2) preserving the model's original functionality on benign inputs—crucial for avoiding detection and ensuring stealth.
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We category backdoor attacks into two types: poisoning-based backdoor attacks and non-poisoning-based backdoor attacks and go deep through essential works in recent years.
Nov 6, 2025
The evolution of Artificial Intelligence (AI) holds colossal promise for automating tasks, driving innovation, and positively impacting industries. However, as AI becomes increasingly pervasive, it is crucial to establish trust in algorithmic decision-making, eliminating bias and ensuring fair outcomes. Adversarial attacks pose a significant threat to AI systems, with backdoor attacks emerging ...
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Nov 13, 2025
Sep 27, 2024
However, all of the existing studies only fo-cus on backdoor attacks against LLMs under the single-turn interaction setting, without exploring the realm of backdoor attacks against chat models in a multi-turn interaction setting.
Oct 9, 2025
A backdoor attack is a type of training-time data poisoning attack that implant backdoor triggers into machine learning models by injecting the trigger pattern(s) into a small proportion of the training data [1]. It aims to trick the model to learn a strong but task-irrelevant correlation between the trigger pattern and a target class, and optimizes three objectives: stealthiness of the ...
Nov 5, 2024
Existing backdoor attacks on code models use unstealthy and easy-to-detect triggers. This paper aims to investigate the vulnerability of code models with stealthy backdoor attacks.
Nov 4, 2024
⚔🛡 Awesome Backdoor Attacks and Defenses This repository contains a collection of papers and resources on backdoor attacks and backdoor defense in deep learning.
Recently, backdoor attacks pose a major security threat to deep neural networks. The attacker embeds hidden malicious behaviors into deep learning models that is activated only when the input contains specific triggers. Although backdoor attacks have a high success rate and are very stealthy, existing backdoor models often lose their attack capabilities after transfer learning. A critical ...
However, the behaviour of such optimisation has not been studied in the light of a fundamentally different class of attacks called backdoors. In this paper, we study how to inject and defend against backdoor attacks for robust models trained using PGD-based robust optimisation. We demonstrate that these models are susceptible to backdoor attacks.
Oct 23, 2024
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May 2, 2024
This post is part of a series about machine learning and artificial intelligence. Click on the blog tag "huskyai" to see related posts. Overview: …
Oct 19, 2025
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Last year Rose Kalemba wrote a blog post explaining how hard it had been - when she was raped as a 14-year-old girl - to get a video of the attack removed from a popular porn website.
In this paper, we propose a multi-model selective backdoor attack method that misleads each model to misclassify samples into a different class according to the position of the trigger.
From this view, we identify two inherent characteristics of backdoor attacks as their weaknesses: 1) the models learn backdoored data much faster than learning with clean data, and the stronger the attack the faster the model converges on backdoored data; 2) the backdoor task is tied to a specific class (the backdoor target class).
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