arXiv subjects

Laboni Sarker

Publications and source records attributed to Laboni Sarker.

4 recordsLinked to original sources

arXiv2026

Quantitative Symbolic Patch Impact Analysis

Traditional equivalence checking classifies programs as equivalent or non-equivalent, providing insufficient information for tasks like patch impact analysis where it is expected the patched version of the program to be non-equivalent to the original program. When two program versions are non-equivalent, determining under what conditions they differ and what percentage of inputs are affected remains an open challenge. In this work, we introduce quantitative partial equivalence analysis, an approach for assessing software patches by quantifying behavioral differences between the original (vulnerable) code and the patched code. Using symbolic analysis, we identify input conditions under which patched and original programs exhibit identical or divergent behaviors. Our approach refines non-equivalence by measuring the extent of behavioral divergence across the input domain. For efficient quantitative analysis of numerical domains, we propose a range-based search heuristic that provides a sound lower bound on equivalence. We demonstrate our approach on 90 CVE patches from widely used open-source projects (Linux, Qemu, FFmpeg), as well as on a Juliet Test Suite-based dataset containing programs with CWEs. Our results show that quantitative partial equivalence analysis effectively characterizes and quantifies patch impact. Additionally, experiments on the EqBench benchmark reveal five C program pairs that are mislabeled as equivalent, and we identify the input conditions under which their behaviors diverge.

cs.PL↗

arXiv2024

UEFI Vulnerability Signature Generation using Static and Symbolic Analysis

Since its major release in 2006, the Unified Extensible Firmware Interface (UEFI) has become the industry standard for interfacing a computer's hardware and operating system, replacing BIOS. UEFI has higher privileged security access to system resources than any other software component, including the system kernel. Hence, identifying and characterizing vulnerabilities in UEFI is extremely important for computer security. However, automated detection and characterization of UEFI vulnerabilities is a challenging problem. Static vulnerability analysis techniques are scalable but lack precision (reporting many false positives), whereas symbolic analysis techniques are precise but are hampered by scalability issues due to path explosion and the cost of constraint solving. In this paper, we introduce a technique called STatic Analysis guided Symbolic Execution (STASE), which integrates both analysis approaches to leverage their strengths and minimize their weaknesses. We begin with a rule-based static vulnerability analysis on LLVM bitcode to identify potential vulnerability targets for symbolic execution. We then focus symbolic execution on each target to achieve precise vulnerability detection and signature generation. STASE relies on the manual specification of reusable vulnerability rules and attacker-controlled inputs. However, it automates the generation of harnesses that guide the symbolic execution process, addressing the usability and scalability of symbolic execution, which typically requires manual harness generation to reduce the state space. We implemented and applied STASE to the implementations of UEFI code base. STASE detects and generates vulnerability signatures for 5 out of 9 recently reported PixieFail vulnerabilities and 13 new vulnerabilities in Tianocore's EDKII codebase.

cs.CR↗

arXiv2024

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation

Rapid advances in the field of Large Language Models (LLMs) have made LLM-based code generation an important area for investigation. An LLM-based code generator takes a prompt as input and produces code that implements the requirements specified in the prompt. Many software requirements include mathematical formulas that specify the expected behavior of the code to be generated. Given a code generation prompt that contains a mathematical formula, a reasonable expectation is that, if the formula is syntactically modified without changing its semantics, the generated code for the modified prompt should be semantically equivalent. We formalize this concept as syntactic robustness and investigate the syntactic robustness of LLMs as code generators. Our experimental assessment demonstrates that LLMs are not syntactically robust for code generation prompts with formulas, especially for the ones that require mathematical reasoning. We investigate attack strategies that can further deteriorate the syntactic robustness of LLMs. Finally, to mitigate syntactic robustness failures in LLMs, we propose a pre-processing step that uses reductions to transform formulas in prompts to a simplified form. Our experimental results demonstrate that the syntactic robustness of LLM-based code generation improves significantly using our approach, improving syntactic robustness of LLMs from 54.05% to 74.42%.

cs.SE↗

arXiv2022

Rare-Seed Generation for Fuzzing

Starting with a random initial seed, fuzzers search for inputs that trigger bugs or vulnerabilities. However, fuzzers often fail to generate inputs for program paths guarded by restrictive branch conditions. In this paper, we show that by first identifying rare-paths in programs (i.e., program paths with path constraints that are unlikely to be satisfied by random input generation), and then, generating inputs/seeds that trigger rare-paths, one can improve the coverage of fuzzing tools. In particular, we present techniques 1) that identify rare paths using quantitative symbolic analysis, and 2) generate inputs that can explore these rare paths using path-guided concolic execution. We provide these inputs as initial seed sets to three state of the art fuzzers. Our experimental evaluation on a set of programs (that contain a lot of restrictive branch conditions) shows that the fuzzers achieve better coverage with the rare-path based seed set compared to a random initial seed.

cs.SE↗