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.
Game of Trojans: Adaptive Adversaries Against Output-based Trojaned-Model Detectors
We propose and analyze an adaptive adversary that can retrain a Trojaned DNN and is also aware of SOTA output-based Trojaned model detectors.
Double-Dip: Thwarting Label-Only Membership Inference Attacks with Transfer Learning and Randomization
However, the suitability of TL as a solution to reduce vulnerability of overfitted DNNs to privacy attacks is unexplored.
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.
MDTD: A Multi Domain Trojan Detector for Deep Neural Networks
An adversary carrying out a backdoor attack embeds a predefined perturbation called a trigger into a small subset of input samples and trains the DNN such that the presence of the trigger in the input results in an adversary-desired output class.
Risk-Aware Distributed Multi-Agent Reinforcement Learning
We then propose a distributed MARL algorithm called the CVaR QD-Learning algorithm, and establish that value functions of individual agents reaches consensus.
LDL: A Defense for Label-Based Membership Inference Attacks
Overfitted models have been shown to be susceptible to query-based attacks such as membership inference attacks (MIAs).
Game of Trojans: A Submodular Byzantine Approach
The results show that (i) with Submodular Trojan algorithm, the adversary needs to embed a Trojan trigger into a very small fraction of samples to achieve high accuracy on both Trojan and clean samples, and (ii) the MM Trojan algorithm yields a trained Trojan model that evades detection with probability 1.
Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning
We term this category of attacks multi-target backdoor attacks.
Privacy-Preserving Reinforcement Learning Beyond Expectation
Through empirical evaluations, we highlight a privacy-utility tradeoff and demonstrate that the RL agent is able to learn behaviors that are aligned with that of a human user in the same environment in a privacy-preserving manner
Shaping Advice in Deep Reinforcement Learning
We design two algorithms- Shaping Advice in Single-agent reinforcement learning (SAS) and Shaping Advice in Multi-agent reinforcement learning (SAM).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning
In this paper, we introduce Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning (AREL) to address these two challenges.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Resilience to Denial-of-Service and Integrity Attacks: A Structured Systems Approach
The resilience of cyberphysical systems to denial-of-service (DoS) and integrity attacks is studied in this paper.
Reinforcement Learning Beyond Expectation
In this paper, we consider a setting where an autonomous agent has to learn behaviors in an unknown environment.
Shaping Advice in Deep Multi-Agent Reinforcement Learning
We observe that using SAM results in agents learning policies to complete tasks faster, and obtain higher rewards than: i) using sparse rewards alone; ii) a state-of-the-art reward redistribution method.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Safety-Critical Online Control with Adversarial Disturbances
We propose an iterative approach to the synthesis of the controller by solving a modified discrete-time Riccati equation.
FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback
The output of the feedback neural network is converted to a shaping reward that is augmented to the reward provided by the environment.
Potential-Based Advice for Stochastic Policy Learning
This paper augments the reward received by a reinforcement learning agent with potential functions in order to help the agent learn (possibly stochastic) optimal policies.
