3 citations · 3 across the 8 of their papers we have counts for
8 papers
Learning Correct Behavior from Examples: Validating Sequential Execution in Autonomous Agents
Reshabh K Sharma, Gaurav Mittal, Yu Hu
As autonomous agents become increasingly sophisticated, validating their sequential behavior presents a significant challenge. Traditional testing approaches require manual specifi…
AC4A: Access Control for Agents
Reshabh K Sharma, Dan Grossman
Large Language Model (LLM) agents combine the chat interaction capabilities of LLMs with the power to interact with external tools and APIs. This enables them to perform complex ta…
Willful Disobedience: Automatically Detecting Failures in Agentic Traces
Reshabh K Sharma, Shraddha Barke, Benjamin Zorn
AI agents are increasingly embedded in real software systems, where they execute multi-step workflows through multi-turn dialogue, tool invocations, and intermediate decisions. The…
Beyond OAuth: Task-Scoped Authorization for AI Agents via Natural Language Slices
Reshabh K Sharma, Linxi Jiang, Shuo Chen +1
AI agents increasingly execute users' natural-language (NL) tasks by calling Web services, yet today's Web authorizes these calls through OAuth, which grants permissions over opera…
ContextCov: Deriving and Enforcing Executable Constraints from Agent Instruction Files
Reshabh K Sharma
As Large Language Model (LLM) agents increasingly execute complex, autonomous software engineering tasks, developers rely on natural language instruction files such as AGENTS.md to…
SplittingSecrets: A Compiler-Based Defense for Preventing Data Memory-Dependent Prefetcher Side-Channels
Reshabh K Sharma, Dan Grossman, David Kohlbrenner
Traditional side-channels take advantage of secrets being used as inputs to unsafe instructions, used for memory accesses, or used in control flow decisions. Constant-time programm…
PromptPex: Automatic Test Generation for Language Model Prompts
Reshabh K Sharma, Jonathan De Halleux, Shraddha Barke +2
Large language models (LLMs) are being used in many applications and prompts for these models are integrated into software applications as code-like artifacts. These prompts behave…
SPML: A DSL for Defending Language Models Against Prompt Attacks
Reshabh K Sharma, Vinayak Gupta, Dan Grossman
Large language models (LLMs) have profoundly transformed natural language applications, with a growing reliance on instruction-based definitions for designing chatbots. However, po…