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20202026
most citedStateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks

15 citations · 32 across the 17 of their papers we have counts for

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Showing 2025Show all

5 papers · 1 filter

cs.SE2025

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…

cs.CR2025★ 1 cited

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…

cs.LG2025

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…

cs.CR2025

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…

cs.CR2025★ 3 cited

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…