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Found 7 papers, 4 papers with code
On Evaluating The Performance of Watermarked Machine-Generated Texts Under Adversarial Attacks
Large Language Models (LLMs) excel in various applications, including text generation and complex tasks.
Jailbreak Attacks and Defenses Against Large Language Models: A Survey
In this paper, we propose a comprehensive and detailed taxonomy of jailbreak attack and defense methods.
JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models
Jailbreak attacks aim to induce Large Language Models (LLMs) to generate harmful responses for forbidden instructions, presenting severe misuse threats to LLMs.
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging
Model merging is a promising lightweight model empowerment technique that does not rely on expensive computing devices (e. g., GPUs) or require the collection of specific training data.
FigStep: Jailbreaking Large Vision-language Models via Typographic Visual Prompts
Ensuring the safety of artificial intelligence-generated content (AIGC) is a longstanding topic in the artificial intelligence (AI) community, and the safety concerns associated with Large Language Models (LLMs) have been widely investigated.
SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders
Recent research has shown that the machine learning model's copyright is threatened by model stealing attacks, which aim to train a surrogate model to mimic the behavior of a given model.
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images
Our experiments demonstrate our method is able to correctly identify the bivariate causal relationship between concepts in images and the representation learned enables a do-calculus manipulation to images, which generates artificial images that might possibly break the physical law depending on where we intervene the causal system.
