1 citations · 1 across the 11 of their papers we have counts for
12 papers
A Trust Ledger and an Execution Check for CPG-Based C-to-Lean 4 Autoformalization: Separating Declined from Silently Incorrect Translations
Ishan K Singavarapu, Manish Bhatt
Verifying large C codebases requires translating them into formally checkable semantics, but most autoformalization work reports a single aggregate success rate that conflates two…
World-Time Compute with Verified Code World Models
James Schwoebel, Ingrida Semenec, Jenia Rousseva +8
LLMs generalize across a domain only after seeing many real, labeled examples, which most domains lack. We study a way to manufacture it cheaply. When a domain's dynamics can be wr…
The Defense Trilemma: Why Prompt Injection Defense Wrappers Fail?
Manish Bhatt, Sarthak Munshi, Vineeth Sai Narajala +6
We prove that no continuous, utility-preserving wrapper defense-a function that preprocesses inputs before the model sees them-can make all outputs strictly safe for a…
LAAF: Logic-layer Automated Attack Framework A Systematic Red-Teaming Methodology for LPCI Vulnerabilities in Agentic Large Language Model Systems
Hammad Atta, Ken Huang, Kyriakos Rock Lambros +11
Agentic LLM systems equipped with persistent memory, RAG pipelines, and external tool connectors face a class of attacks - Logic-layer Prompt Control Injection (LPCI) - for which n…
Manifold of Failure: Behavioral Attraction Basins in Language Models
Sarthak Munshi, Manish Bhatt, Vineeth Sai Narajala +4
While prior work has focused on projecting adversarial examples back onto the manifold of natural data to restore safety, we argue that a comprehensive understanding of AI safety r…
Predictive Coding and Information Bottleneck for Hallucination Detection in Large Language Models
Manish Bhatt
Hallucinations in Large Language Models (LLMs) -- generations that are plausible but factually unfaithful -- remain a critical barrier to high-stakes deployment. Current detection…
Large Empirical Case Study: Go-Explore adapted for AI Red Team Testing
Manish Bhatt, Adrian Wood, Idan Habler +1
Production LLM agents with tool-using capabilities require security testing despite their safety training. We adapt Go-Explore to evaluate GPT-4o-mini across 28 experimental runs s…
MAIF: Enforcing AI Trust and Provenance with an Artifact-Centric Agentic Paradigm
Vineeth Sai Narajala, Manish Bhatt, Idan Habler +2
The AI trustworthiness crisis threatens to derail the artificial intelligence revolution, with regulatory barriers, security vulnerabilities, and accountability gaps preventing dep…
Logic layer Prompt Control Injection (LPCI): A Novel Security Vulnerability Class in Agentic Systems
Hammad Atta, Ken Huang, Manish Bhatt +3
The integration of large language models (LLMs) into enterprise systems has introduced a new class of covert security vulnerabilities, particularly within logic execution layers an…
Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
Manish Bhatt
The Bhatt Conjectures framework introduces rigorous, hierarchical benchmarks for evaluating AI reasoning and understanding, moving beyond pattern matching to assess representation…
COALESCE: Economic and Security Dynamics of Skill-Based Task Outsourcing Among Team of Autonomous LLM Agents
Manish Bhatt, Ronald F. Del Rosario, Vineeth Sai Narajala +1
The meteoric rise and proliferation of autonomous Large Language Model (LLM) agents promise significant capabilities across various domains. However, their deployment is increasing…
ETDI: Mitigating Tool Squatting and Rug Pull Attacks in Model Context Protocol (MCP) by using OAuth-Enhanced Tool Definitions and Policy-Based Access Control
Manish Bhatt, Vineeth Sai Narajala, Idan Habler
The Model Context Protocol (MCP) plays a crucial role in extending the capabilities of Large Language Models (LLMs) by enabling integration with external tools and data sources. Ho…