2 citations · 2 across the 5 of their papers we have counts for
5 papers
Cascade: Composing Software-Hardware Attack Gadgets for Adversarial Threat Amplification in Compound AI Systems
Sarbartha Banerjee, Prateek Sahu, Anjo Vahldiek-Oberwagner +2
Rapid progress in generative AI has given rise to Compound AI systems - pipelines comprised of multiple large language models (LLM), software tools and database systems. Compound A…
Towards Reinforcement Learning for Exploration of Speculative Execution Vulnerabilities
Evan Lai, Wenjie Xiong, Edward Suh +2
Speculative attacks such as Spectre can leak secret information without being discovered by the operating system. Speculative execution vulnerabilities are finicky and deep in the…
SoK: A Systems Perspective on Compound AI Threats and Countermeasures
Sarbartha Banerjee, Prateek Sahu, Mulong Luo +3
Large language models (LLMs) used across enterprises often use proprietary models and operate on sensitive inputs and data. The wide range of attack vectors identified in prior res…
Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Accelerators
Sarbartha Banerjee, Shijia Wei, Prakash Ramrakhyani +1
Trusted execution environments (TEEs) for machine learning accelerators are indispensable in secure and efficient ML inference. Optimizing workloads through state-space exploration…
ConfusedPilot: Confused Deputy Risks in RAG-based LLMs
Ayush RoyChowdhury, Mulong Luo, Prateek Sahu +2
Retrieval augmented generation (RAG) is a process where a large language model (LLM) retrieves useful information from a database and then generates the responses. It is becoming p…