arXiv subjects

Tapti Palit

Publications and source records attributed to Tapti Palit.

5 recordsLinked to original sources

arXiv2026

ORBIT: Guided Agentic Orchestration for Autonomous C-to-Rust Transpilation

Large-scale migration of legacy C code to Rust offers a promising path toward improving memory safety, but LLM-based C-to-Rust translation remains challenging due to limited context windows and hallucinations. Prior approaches are evaluated primarily on small programs or datasets skewed toward small codebases, providing limited insight into scalability on real-world systems. They also rely on static context construction, which breaks down in the presence of complex cross-module dependencies and often requires manual intervention. Recent coding agents offer a promising alternative through dynamic codebase navigation and context curation. When used out of the box, however, they frequently produce incomplete translations that appear superficially correct. We present ORBIT, an autonomous agentic framework for project-level C-to-Rust translation that combines dynamic context collection with dependency-guided orchestration and iterative verification. ORBIT constructs a dependency-aware translation graph, generates Rust interfaces, maps C functions to Rust counterparts, and coordinates multiple specialized agents. We evaluate ORBIT on 24 programs from CRUST-Bench, with 91.7% of the programs exceeding 1,000 lines of code. ORBIT achieves 100% compilation success and 91.7% test success in both expert-interface and automatically generated-interface settings, substantially outperforming C2Rust and CRUST-Bench, while reducing unsafe Rust code blocks to nearly zero. We further evaluate ORBIT on challenging cases from the DARPA TRACTOR benchmark, where it achieves competitive performance relative to participating systems.

cs.SE↗

arXiv2025

PhaseSeed: Precise Call Graph Construction for Split-Phase Applications using Dynamic Seeding

Precise and sound call graph construction is crucial for many software security mechanisms. Unfortunately, traditional static pointer analysis techniques used to generate application call graphs suffer from imprecision. These techniques are agnostic to the application's architecture and are designed for broad applicability. To mitigate this precision problem, we propose PhaseSeed, a novel technique that improves the accuracy of pointer analysis for split-phase applications, which have distinct initialization and processing phases. PhaseSeed analyzes the initialization phase dynamically, collecting the points-to relationships established at runtime. At the end of the initialization phase, it then seeds this information to a static analysis stage that performs pointer analysis for all code that stays in scope during the processing phase, improving precision. Our observations show that, given the same runtime configuration options, the points-to relationships established during the initialization phase remain constant across multiple runs. Therefore, PhaseSeed is sound with respect to a given initial configuration. We apply PhaseSeed to three security mechanisms: control flow integrity (CFI), software debloating, and system call filtering. PhaseSeed provides up to 92.6% precision improvement for CFI compared to static call graph construction techniques, and filters nine additional security-critical system calls when used to generate Seccomp profiles.

cs.SE↗

arXiv2025

RustAssure: Differential Symbolic Testing for LLM-Transpiled C-to-Rust Code

Rust is a memory-safe programming language that significantly improves software security. Existing codebases written in unsafe memory languages, such as C, must first be transpiled to Rust to take advantage of Rust's improved safety guarantees. RustAssure presents a system that uses Large Language Models (LLMs) to automatically transpile existing C codebases to Rust. RustAssure uses prompt engineering techniques to maximize the chances of the LLM generating idiomatic and safe Rust code. Moreover, because LLMs often generate code with subtle bugs that can be missed under traditional unit or fuzz testing, RustAssure performs differential symbolic testing to establish the semantic similarity between the original C and LLM-transpiled Rust code. We evaluated RustAssure with five real-world applications and libraries, and showed that our system is able to generate compilable Rust functions for 89.8% of all C functions, of which 69.9% produced equivalent symbolic return values for both the C and Rust functions.

cs.SE↗

arXiv2025

SafeTrans: LLM-assisted Transpilation from C to Rust

Rust is a strong contender for a memory-safe alternative to C as a "systems" language, but porting the vast amount of existing C code to Rust remains daunting. In this paper, we evaluate the potential of large language models (LLMs) to automate the transpilation of C code to idiomatic Rust. We present SafeTrans, a generic framework that leverages LLMs to i) transpile C code into Rust, and ii) iteratively repair compilation and runtime errors. A key novelty of our approach is a few-shot guided repair technique for translation errors, which provides contextual information and example code snippets for specific error types, guiding the LLM toward the correct solution. Another novel aspect of our work is the evaluation of the security implications of the transpilation process, showing how some vulnerability classes in C persist in the translated Rust code. SafeTrans was evaluated with six leading LLMs on 2,653 C programs and two real-world C projects. Our results show that iterative repair improves the rate of successful translations from 54% to 80% for the best-performing LLM (gpt-4o).

cs.CR↗

arXiv2025

A VM-HDL Co-Simulation Framework for Systems with PCIe-Connected FPGAs

PCIe-connected FPGAs are gaining popularity as an accelerator technology in data centers. However, it is challenging to jointly develop and debug host software and FPGA hardware. Changes to the hardware design require a time-consuming FPGA synthesis process, and modification to the software, especially the operating system and device drivers, can frequently cause the system to hang, without providing enough information for debugging. The combination of these problems results in long debug iterations and a slow development process. To overcome these problems, we designed a VM-HDL co-simulation framework, which is capable of running the same software, operating system, and hardware designs as the target physical system, while providing full visibility and significantly shorter debug iterations.

cs.DC↗