arXiv subjects

Saad Ullah

Publications and source records attributed to Saad Ullah.

5 recordsLinked to original sources

arXiv2026

From Documentation to Zero-day Vulnerabilities: LLM-Driven Fuzzing of JavaScript Engines in PDF Readers

Existing fuzzers for PDF readers rely on simple test cases that involve only individual API calls, leading to limited coverage and potentially missing vulnerabilities that require sequences of API calls. To address these limitations, we propose PDFuzzer, a novel PDF engine fuzzer that automatically generates complex and meaningful API call sequences. PDFuzzer first uses a Large Language Model (LLM) to construct context-free grammars and infer the relationships between individual API calls from specifications extracted from JavaScript API manuals and execution traces. Based on the grammars and relationships, PDFuzzer employs a constraint solver to generate concrete API call sequences for fuzzing. Our experiments show that PDFuzzer significantly outperforms state-of-the-art PDF fuzzers (TypeOracle, Favocado, and Cooper) and LLM-based fuzzers (Fuzz4All, naive LLM) on three mainstream PDF readers: Adobe Acrobat Reader, Foxit PDF Reader, and PDF-XChange Editor. PDFuzzer achieves up to 48% higher coverage than existing tools and identifies 31 zero-day vulnerabilities in these readers, from information leakage to arbitrary code execution. Our ablation study validates the necessity of each component, including LLMs, which achieve high accuracy across all pipeline stages (93-98%). We disclosed all vulnerabilities to the vendors via a coordinated vulnerability disclosure process and received bug bounties.

cs.CR↗

arXiv2026

Multi-Agent Taint Specification Extraction for Vulnerability Detection

Static Application Security Testing (SAST) tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results compared to traditional pattern-based approaches. However, performing static taint analysis for JavaScript poses two major challenges. First, JavaScript's dynamic features complicate data flow extraction required for taint tracking. Second, npm's large library ecosystem makes it difficult to identify relevant sources/sinks and establish taint propagation across dependencies. In this paper, we present SemTaint, a multi-agent system that strategically combines the semantic understanding of Large Language Models (LLMs) with traditional static program analysis to extract taint specifications, including sources, sinks, call edges, and library flow summaries tailored to each package. Conceptually, SemTaint uses static program analysis to calculate a call graph and defers to an LLM to resolve call edges that cannot be resolved statically. Further, it uses the LLM to classify sources and sinks for a given CWE. The resulting taint specification is then provided to a SAST tool, which performs vulnerability analysis. We integrate SemTaint with CodeQL, a state-of-the-art SAST tool, and demonstrate its effectiveness by detecting 106 of 162 vulnerabilities previously undetectable by CodeQL. Furthermore, we find 4 novel vulnerabilities in 4 popular npm packages. In doing so, we demonstrate that LLMs can practically enhance existing static program analysis algorithms, combining the strengths of both symbolic reasoning and semantic understanding for improved vulnerability detection.

cs.CR↗

arXiv2025

From CVE Entries to Verifiable Exploits: An Automated Multi-Agent Framework for Reproducing CVEs

High-quality datasets of real-world vulnerabilities and their corresponding verifiable exploits are crucial resources in software security research. Yet such resources remain scarce, as their creation demands intensive manual effort and deep security expertise. In this paper, we present CVE-GENIE, an automated, large language model (LLM)-based multi-agent framework designed to reproduce real-world vulnerabilities, provided in Common Vulnerabilities and Exposures (CVE) format, to enable creation of high-quality vulnerability datasets. Given a CVE entry as input, CVE-GENIE gathers the relevant resources of the CVE, automatically reconstructs the vulnerable environment, and (re)produces a verifiable exploit. Our systematic evaluation highlights the efficiency and robustness of CVE-GENIE's design and successfully reproduces approximately 51% (428 of 841) CVEs published in 2024-2025, complete with their verifiable exploits, at an average cost of $2.77 per CVE. Our pipeline offers a robust method to generate reproducible CVE benchmarks, valuable for diverse applications such as fuzzer evaluation, vulnerability patching, and assessing AI's security capabilities.

cs.CR↗

arXiv2023

LLMs Cannot Reliably Identify and Reason About Security Vulnerabilities (Yet?): A Comprehensive Evaluation, Framework, and Benchmarks

Large Language Models (LLMs) have been suggested for use in automated vulnerability repair, but benchmarks showing they can consistently identify security-related bugs are lacking. We thus develop SecLLMHolmes, a fully automated evaluation framework that performs the most detailed investigation to date on whether LLMs can reliably identify and reason about security-related bugs. We construct a set of 228 code scenarios and analyze eight of the most capable LLMs across eight different investigative dimensions using our framework. Our evaluation shows LLMs provide non-deterministic responses, incorrect and unfaithful reasoning, and perform poorly in real-world scenarios. Most importantly, our findings reveal significant non-robustness in even the most advanced models like `PaLM2' and `GPT-4': by merely changing function or variable names, or by the addition of library functions in the source code, these models can yield incorrect answers in 26% and 17% of cases, respectively. These findings demonstrate that further LLM advances are needed before LLMs can be used as general purpose security assistants.

cs.CR↗

arXiv2023

WASEF: Web Acceleration Solutions Evaluation Framework

The World Wide Web has become increasingly complex in recent years. This complexity severely affects users in the developing regions due to slow cellular data connectivity and usage of low-end smartphone devices. Existing solutions to simplify the Web are generally evaluated using several different metrics and settings, which hinders the comparison of these solutions against each other. Hence, it is difficult to select the appropriate solution for a specific context and use case. This paper presents Wasef, a framework that uses a comprehensive set of timing, saving, and quality metrics to evaluate and compare different web complexity solutions in a reproducible manner and under realistic settings. The framework integrates a set of existing state-of-the-art solutions and facilitates the addition of newer solutions down the line. Wasef first creates a cache of web pages by crawling both landing and internal ones. Each page in the cache is then passed through a web complexity solution to generate an optimized version of the page. Finally, each optimized version is evaluated in a consistent manner using a uniform environment and metrics. We demonstrate how the framework can be used to compare and contrast the performance characteristics of different web complexity solutions under realistic conditions. We also show that the accessibility to pages in developing regions can be significantly improved, by evaluating the top 100 global pages in the developed world against the top 100 pages in the lowest 50 developing countries. Results show a significant difference in terms of complexity and a potential benefit for our framework in improving web accessibility in these countries.

cs.PF↗