Search Results for author:
Found 18 papers, 6 papers with code
ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs
In this paper, we propose a novel ASCII art-based jailbreak attack and introduce a comprehensive benchmark Vision-in-Text Challenge (ViTC) to evaluate the capabilities of LLMs in recognizing prompts that cannot be solely interpreted by semantics.
BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models
Moreover, we show that LLMs endowed with stronger reasoning capabilities exhibit higher susceptibility to BadChain, exemplified by a high average attack success rate of 97. 0% across the six benchmark tasks on GPT-4.
CBD: A Certified Backdoor Detector Based on Local Dominant Probability
Notably, for backdoor attacks with random perturbation triggers bounded by $\ell_2\leq0. 75$ which achieves more than 90\% attack success rate, CBD achieves 100\% (98\%), 100\% (84\%), 98\% (98\%), and 72\% (40\%) empirical (certified) detection true positive rates on the four benchmark datasets GTSRB, SVHN, CIFAR-10, and TinyImageNet, respectively, with low false positive rates.
Backdoor Mitigation by Correcting the Distribution of Neural Activations
Backdoor (Trojan) attacks are an important type of adversarial exploit against deep neural networks (DNNs), wherein a test instance is (mis)classified to the attacker's target class whenever the attacker's backdoor trigger is present.
Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection
Deep neural networks are vulnerable to backdoor attacks (Trojans), where an attacker poisons the training set with backdoor triggers so that the neural network learns to classify test-time triggers to the attacker's designated target class.
UMD: Unsupervised Model Detection for X2X Backdoor Attacks
Backdoor (Trojan) attack is a common threat to deep neural networks, where samples from one or more source classes embedded with a backdoor trigger will be misclassified to adversarial target classes.
MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic
Our detector leverages the influence of the backdoor attack, independent of the backdoor embedding mechanism, on the landscape of the classifier's outputs prior to the softmax layer.
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios
We show that our ET statistic is effective {\it using the same detection threshold}, irrespective of the classification domain, the attack configuration, and the BP reverse-engineering algorithm that is used.
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks
A DNN being attacked will predict to an attacker-desired target class whenever a test sample from any source class is embedded with a backdoor pattern; while correctly classifying clean (attack-free) test samples.
Detecting Backdoor Attacks Against Point Cloud Classifiers
Backdoor attacks (BA) are an emerging threat to deep neural network classifiers.
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers
Data Poisoning (DP) is an effective attack that causes trained classifiers to misclassify their inputs.
L-RED: Efficient Post-Training Detection of Imperceptible Backdoor Attacks without Access to the Training Set
Unfortunately, most existing REDs rely on an unrealistic assumption that all classes except the target class are source classes of the attack.
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing
The attacker poisons the training set with a relatively small set of images from one (or several) source class(es), embedded with a backdoor pattern and labeled to a target class.
Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic
Here, we address post-training detection of innocuous perceptible backdoors in DNN image classifiers, wherein the defender does not have access to the poisoned training set, but only to the trained classifier, as well as unpoisoned examples.
Notes on Margin Training and Margin p-Values for Deep Neural Network Classifiers
We provide a new local class-purity theorem for Lipschitz continuous DNN classifiers.
Detection of Backdoors in Trained Classifiers Without Access to the Training Set
Here we address post-training detection of backdoor attacks in DNN image classifiers, seldom considered in existing works, wherein the defender does not have access to the poisoned training set, but only to the trained classifier itself, as well as to clean examples from the classification domain.
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks
After introducing relevant terminology and the goals and range of possible knowledge of both attackers and defenders, we survey recent work on test-time evasion (TTE), data poisoning (DP), and reverse engineering (RE) attacks and particularly defenses against same.
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters
Such attacks are successful mainly because of the poor representation power of the naive Bayes (NB) model, with only a single (component) density to represent spam (plus a possible attack).
