Search Results for author:
Found 17 papers, 4 papers with code
PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval
In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners.
Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks
Control barrier function (CBF)-based approaches have been proposed to guarantee the safety of robotic systems.
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.
SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding
Our results show that SafeDecoding significantly reduces the attack success rate and harmfulness of jailbreak attacks without compromising the helpfulness of responses to benign user queries.
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.
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning
Our results show that the global model learned with Brave in the presence of adversaries achieves comparable classification accuracy to a global model trained in the absence of any adversary.
Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications
Successful exploits of the identified vulnerabilities result in the users receiving responses tailored to the intent of a threat initiator.
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).
Abstraction-Free Control Synthesis to Satisfy Temporal Logic Constraints under Sensor Faults and Attacks
We develop a class of fault-tolerant finite time convergence control barrier functions (CBFs) to guarantee that a dynamical system reaches a set within finite time almost surely in the presence of malicious attacks.
Barrier Certificate based Safe Control for LiDAR-based Systems under Sensor Faults and Attacks
We prove that the synthesized control input guarantees system safety using control barrier certificates.
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.
Safety-Critical Control Synthesis for Unknown Sampled-Data Systems via Control Barrier Functions
By satisfying the constructed CBF constraint at each sampling time, we guarantee the unknown sampled-data system is safe for all time.
A Game-Theoretic Framework for Controlled Islanding in the Presence of Adversaries
In this paper, we study the controlled islanding problem of a power system under disturbances introduced by a malicious adversary.
Reinforcement Learning Beyond Expectation
In this paper, we consider a setting where an autonomous agent has to learn behaviors in an unknown environment.
LQG Reference Tracking with Safety and Reachability Guarantees under Unknown False Data Injection Attacks
For each possible set of compromised sensors, we maintain a state estimator disregarding the sensors in that set, and calculate the optimal LQG control input at each time based on this estimate.
