Vital: Vulnerability-Oriented Symbolic Execution via Type-Unsafe Pointer-Guided Monte Carlo Tree Search

• URL: http://arxiv.org/abs/2408.08772v1
• Date: Fri, 16 Aug 2024 14:29:57 GMT
• Title: Vital: Vulnerability-Oriented Symbolic Execution via Type-Unsafe Pointer-Guided Monte Carlo Tree Search
• Authors: Haoxin Tu, Lingxiao Jiang, Marcel Böhme,
• Abstract summary: This paper proposes a new vulnerability-oriented symbolic execution via type-unsafe pointer-guided Monte Carlo Tree Search (MCTS) We show that Vital could cover up to 90.03% more unsafe pointers and detect up to 37.50% more unique memory errors. In the latter, the results show that Vital could achieve a speedup of up to 30x execution time and a reduction of up to 20x memory consumption.
• Score: 18.500951309269396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to find memory safety bugs efficiently when navigating a symbolic execution tree that suffers from path explosion? Existing solutions either adopt path search heuristics to maximize coverage rate or chopped symbolic execution to skip uninteresting code (i.e., manually labeled as vulnerability-unrelated) during path exploration. However, most existing search heuristics are not vulnerability-oriented, and manual labeling of irrelevant code-to-be-skipped relies heavily on prior expert knowledge, making it hard to detect vulnerabilities effectively in practice. This paper proposes Vital, a new vulnerability-oriented symbolic execution via type-unsafe pointer-guided Monte Carlo Tree Search (MCTS). A pointer that is type unsafe cannot be statically proven to be safely dereferenced without memory corruption. Our key hypothesis is that a path with more type unsafe pointers is more likely to contain vulnerabilities. Vital drives a guided MCTS to prioritize paths in the symbolic execution tree that contain a larger number of unsafe pointers and to effectively navigate the exploration-exploitation trade-off. We built Vital on top of KLEE and compared it with existing search strategies and chopped symbolic execution. In the former, the results demonstrate that Vital could cover up to 90.03% more unsafe pointers and detect up to 37.50% more unique memory errors. In the latter, the results show that Vital could achieve a speedup of up to 30x execution time and a reduction of up to 20x memory consumption on automatically detecting known vulnerabilities without prior expert knowledge.

Related papers

• Multi-Pass Targeted Dynamic Symbolic Execution [0.0]
  We present a Multi-Pass Targeted Dynamic Execution approach that starts from a target program location and moves backward until it reaches a specified entry point. Our approach achieves on average 4X reduction in the number of paths explored and 2X speedup.
  arXiv  Detail & Related papers  (2024-08-14T20:14:59Z)
• LightDE: A Lightweight Method for Eliminating Dangling Pointers [0.0]
  Use-After-Free (UAF) vulnerabilities pose a serious threat to software security. Existing methods for defending against UAF vulnerabilities by eliminating dangling pointers need to interrupt the program's execution. We propose a novel approach called LightDE to overcome this drawback.
  arXiv  Detail & Related papers  (2024-05-31T08:47:24Z)
• FV8: A Forced Execution JavaScript Engine for Detecting Evasive Techniques [53.288368877654705]
  FV8 is a modified V8 JavaScript engine designed to identify evasion techniques in JavaScript code. It selectively enforces code execution on APIs that conditionally inject dynamic code. It identifies 1,443 npm packages and 164 (82%) extensions containing at least one type of evasion.
  arXiv  Detail & Related papers  (2024-05-21T19:54:19Z)
• FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs [54.27040631527217]
  We propose a novel framework called FoC to Figure out the Cryptographic functions in stripped binaries. We first build a binary large language model (FoC-BinLLM) to summarize the semantics of cryptographic functions in natural language. We then build a binary code similarity model (FoC-Sim) upon the FoC-BinLLM to create change-sensitive representations and use it to retrieve similar implementations of unknown cryptographic functions in a database.
  arXiv  Detail & Related papers  (2024-03-27T09:45:33Z)
• SeMalloc: Semantics-Informed Memory Allocator [18.04397502953383]
  Use-after-free (UAF) is a critical and prevalent problem in memory unsafe languages. We show one way to balance the trinity by passing more semantics about the heap object to the allocator. In SeMalloc, only heap objects allocated from the same call site and via the same function call stack can possibly share a virtual memory address.
  arXiv  Detail & Related papers  (2024-02-02T21:02:15Z)
• The Vulnerability Is in the Details: Locating Fine-grained Information of Vulnerable Code Identified by Graph-based Detectors [33.395068754566935]
  VULEXPLAINER is a tool for locating vulnerability-critical code lines from coarse-level vulnerable code snippets. It can flag the vulnerability-triggering code statements with an accuracy of around 90% against eight common C/C++ vulnerabilities.
  arXiv  Detail & Related papers  (2024-01-05T10:15:04Z)
• Certifying LLM Safety against Adversarial Prompting [70.96868018621167]
  Large language models (LLMs) are vulnerable to adversarial attacks that add malicious tokens to an input prompt. We introduce erase-and-check, the first framework for defending against adversarial prompts with certifiable safety guarantees.
  arXiv  Detail & Related papers  (2023-09-06T04:37:20Z)
• Precise Zero-Shot Dense Retrieval without Relevance Labels [60.457378374671656]
  Hypothetical Document Embeddings(HyDE) is a zero-shot dense retrieval system. We show that HyDE significantly outperforms the state-of-the-art unsupervised dense retriever Contriever.
  arXiv  Detail & Related papers  (2022-12-20T18:09:52Z)
• Unsafe's Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering toward Finding Memory-safety Bugs via Machine Learning [20.68333298047064]
  Rust provides memory-safe mechanisms to avoid memory-safety bugs in programming. Unsafe code that enhances the usability of Rust provides clear spots for finding memory-safety bugs. We claim that these unsafe spots can still be identifiable in Rust binary code via machine learning.
  arXiv  Detail & Related papers  (2022-10-31T19:32:18Z)
• ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation [106.04777600352743]
  Differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory. The single-path DARTS comes in, which only chooses a single-path submodel at each step. While being memory-friendly, it also comes with low computational costs. We propose a new algorithm called RObustifying Memory-Efficient NAS (ROME) to give a cure.
  arXiv  Detail & Related papers  (2020-11-23T06:34:07Z)
• Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities [47.748732208602355]
  Autosploit is an automated framework for evaluating the exploitability of vulnerabilities. It automatically tests the exploits on different configurations of the environment. It is able to identify the system properties that affect the ability to exploit a vulnerability in both noiseless and noisy environments.
  arXiv  Detail & Related papers  (2020-06-30T18:49:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.