103 citations · 374 across the 18 of their papers we have counts for
12 papers · 1 filter
SelfOp: An Optimization Algorithm for Self-Improving Security Agents
Saad Ullah, Yigitcan Kaya, Christopher Kruegel +2
LLM agents are increasingly used for security tasks: vulnerability discovery, exploit reproduction, and patch generation. Improving them at the model level demands expert demonstra…
From Documentation to Zero-day Vulnerabilities: LLM-Driven Fuzzing of JavaScript Engines in PDF Readers
Suyue Guo, Stijn Pletinckx, Tianle Yu +5
Existing fuzzers for PDF readers rely on simple test cases that involve only individual API calls, leading to limited coverage and potentially missing vulnerabilities that require…
MalwarePT: A Binary-Level Foundation Model for Malware Analysis
Saastha Vasan, Yuzhou Nie, Kaie Chen +6
Automated malware analysis increasingly relies on machine learning, yet most existing methods remain task-specific and depend on handcrafted features or narrowly scoped models. Rec…
When AI Meets the Web: Prompt Injection Risks in Third-Party AI Chatbot Plugins
Yigitcan Kaya, Anton Landerer, Stijn Pletinckx +3
Prompt injection attacks pose a critical threat to large language models (LLMs), with prior work focusing on cutting-edge LLM applications like personal copilots. In contrast, simp…
MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation
Eunjin Roh, Yigitcan Kaya, Christopher Kruegel +2
We present MADCAT, a self-supervised approach designed to address the concept drift problem in malware detection. MADCAT employs an encoder-decoder architecture and works by test-t…
ML-Based Behavioral Malware Detection Is Far From a Solved Problem
Yigitcan Kaya, Yizheng Chen, Marcus Botacin +5
Malware detection is a ubiquitous application of Machine Learning (ML) in security. In behavioral malware analysis, the detector relies on features extracted from program execution…
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
Shoumik Saha, Wenxiao Wang, Yigitcan Kaya +2
Machine Learning (ML) models have been utilized for malware detection for over two decades. Consequently, this ignited an ongoing arms race between malware authors and antivirus sy…
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
Sanghyun Hong, Varun Chandrasekaran, Yiğitcan Kaya +2
Machine learning algorithms are vulnerable to data poisoning attacks. Prior taxonomies that focus on specific scenarios, e.g., indiscriminate or targeted, have enabled defenses for…
How to 0wn NAS in Your Spare Time
Sanghyun Hong, Michael Davinroy, Yiğitcan Kaya +2
New data processing pipelines and novel network architectures increasingly drive the success of deep learning. In consequence, the industry considers top-performing architectures a…
Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks
Sanghyun Hong, Pietro Frigo, Yiğitcan Kaya +2
Deep neural networks (DNNs) have been shown to tolerate "brain damage": cumulative changes to the network's parameters (e.g., pruning, numerical perturbations) typically result in…
Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks
Sanghyun Hong, Michael Davinroy, Yiǧitcan Kaya +5
Recent work has introduced attacks that extract the architecture information of deep neural networks (DNN), as this knowledge enhances an adversary's capability to conduct black-bo…
Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks
Octavian Suciu, Radu Mărginean, Yiğitcan Kaya +2
Recent results suggest that attacks against supervised machine learning systems are quite effective, while defenses are easily bypassed by new attacks. However, the specifications…