15 citations · 32 across the 17 of their papers we have counts for
19 papers
An unscented universal filter for joint input-state-parameter estimation of a partially measured system: an application to drive-by structural health monitoring
Zihao Liu, Chul-Woo Kim, Mehrisadat Makki Alamdari
Drive-by structural health monitoring infers the condition of a bridge from the response of an instrumented vehicle, whose measurements are dominated by the road roughness and the…
Agent Security is a Systems Problem
Mihai Christodorescu, Earlence Fernandes, Ashish Hooda +11
We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted component, and security invariants…
Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions
Sarthak Choudhary, Atharv Singh Patlan, Nils Palumbo +3
We present Sparse Backdoor, a supply-chain attack that plants a provably undetectable backdoor in pre-trained image classifiers, including convolutional networks and Vision Transfo…
Auto-SPT: Automating Semantic Preserving Transformations for Code
Ashish Hooda, Mihai Christodorescu, Chuangang Ren +3
Machine learning (ML) models for code clone detection determine whether two pieces of code are semantically equivalent, which in turn is a key building block for software-engineeri…
Systems Security Foundations for Agentic Computing
Mihai Christodorescu, Earlence Fernandes, Ashish Hooda +11
In recent years, agentic artificial intelligence (AI) systems are becoming increasingly widespread. These systems allow agents to use various tools, such as web browsers, compilers…
What Really is a Member? Discrediting Membership Inference via Poisoning
Neal Mangaokar, Ashish Hooda, Zhuohang Li +5
Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often…
Through the Stealth Lens: Attention-Aware Defenses Against Poisoning in RAG
Sarthak Choudhary, Nils Palumbo, Ashish Hooda +2
Retrieval-augmented generation (RAG) systems are vulnerable to attacks that inject poisoned passages into the retrieved context, even at low corruption rates. We show that existing…
Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface
Andrey Labunets, Nishit V. Pandya, Ashish Hooda +2
We surface a new threat to closed-weight Large Language Models (LLMs) that enables an attacker to compute optimization-based prompt injections. Specifically, we characterize how an…
Functional Homotopy: Smoothing Discrete Optimization via Continuous Parameters for LLM Jailbreak Attacks
Zi Wang, Divyam Anshumaan, Ashish Hooda +2
Optimization methods are widely employed in deep learning to identify and mitigate undesired model responses. While gradient-based techniques have proven effective for image models…
PolicyLR: A Logic Representation For Privacy Policies
Ashish Hooda, Rishabh Khandelwal, Prasad Chalasani +2
Privacy policies are crucial in the online ecosystem, defining how services handle user data and adhere to regulations such as GDPR and CCPA. However, their complexity and frequent…
CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systems
Guruprasad Viswanathan Ramesh, Ashish Hooda, Shimaa Ahmed +3
Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations and conditions essential. Standard ev…
PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails
Neal Mangaokar, Ashish Hooda, Jihye Choi +4
Large language models (LLMs) are typically aligned to be harmless to humans. Unfortunately, recent work has shown that such models are susceptible to automated jailbreak attacks th…
Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates
Ashish Hooda, Mihai Christodorescu, Miltiadis Allamanis +3
Large Language Models' success on text generation has also made them better at code generation and coding tasks. While a lot of work has demonstrated their remarkable performance o…
Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks
Ashish Hooda, Neal Mangaokar, Ryan Feng +3
Adversarial examples threaten the integrity of machine learning systems with alarming success rates even under constrained black-box conditions. Stateful defenses have emerged as a…
Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks
Ryan Feng, Ashish Hooda, Neal Mangaokar +3
Recent work has proposed stateful defense models (SDMs) as a compelling strategy to defend against a black-box attacker who only has query access to the model, as is common for onl…
SkillFence: A Systems Approach to Practically Mitigating Voice-Based Confusion Attacks
Ashish Hooda, Matthew Wallace, Kushal Jhunjhunwalla +2
Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to vo…
Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance
Ashish Hooda, Andrey Labunets, Tadayoshi Kohno +1
Content scanning systems employ perceptual hashing algorithms to scan user content for illegal material, such as child pornography or terrorist recruitment flyers. Perceptual hashi…
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles
Ashish Hooda, Neal Mangaokar, Ryan Feng +3
Detecting diffusion-generated deepfake images remains an open problem. Current detection methods fail against an adversary who adds imperceptible adversarial perturbations to the d…
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect
Athena Sayles, Ashish Hooda, Mohit Gupta +2
Physical adversarial examples for camera-based computer vision have so far been achieved through visible artifacts -- a sticker on a Stop sign, colorful borders around eyeglasses o…