activity
20232025
most citedTowards Neural Synthesis for SMT-Assisted Proof-Oriented Programming

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2025

Secure Parsing and Serializing with Separation Logic Applied to CBOR, CDDL, and COSE

Tahina Ramananandro, Gabriel Ebner, Guido Martínez +1

Incorrect handling of security-critical data formats, particularly in low-level languages, are the root cause of many security vulnerabilities. Provably correct parsing and seriali…

cs.SE2024

DiffSpec: Differential Testing with LLMs using Natural Language Specifications and Code Artifacts

Nikitha Rao, Elizabeth Gilbert, Harrison Green +4

Differential testing can be an effective way to find bugs in software systems with multiple implementations that conform to the same specification, like compilers, network protocol…

cs.SE2024★ 1 cited

3DGen: AI-Assisted Generation of Provably Correct Binary Format Parsers

Sarah Fakhoury, Markus Kuppe, Shuvendu K. Lahiri +2

Improper parsing of attacker-controlled input is a leading source of software security vulnerabilities, especially when programmers transcribe informal format descriptions in RFCs…

cs.PL2024★ 3 cited

Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming

Saikat Chakraborty, Gabriel Ebner, Siddharth Bhat +4

Proof-oriented programs mix computational content with proofs of program correctness. However, the human effort involved in programming and proving is still substantial, despite th…

cs.PL2023★ 1 cited

Ranking LLM-Generated Loop Invariants for Program Verification

Saikat Chakraborty, Shuvendu K. Lahiri, Sarah Fakhoury +6

Synthesizing inductive loop invariants is fundamental to automating program verification. In this work, we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable…