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Found 6 papers, 2 papers with code
RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content
The innovative use of constrained optimization and a fusion-based guardrail approach represents a significant step forward in developing more secure and reliable LLMs, setting a new standard for content moderation frameworks in the face of evolving digital threats.
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.
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications such as healthcare and finance -- where mistakes can be costly.
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.
Label-Smoothed Backdoor Attack
In order to achieve a high attack success rate using as few poisoned training samples as possible, most existing attack methods change the labels of the poisoned samples to the target class.
