UEFI Memory Forensics: A Framework for UEFI Threat Analysis
- URL: http://arxiv.org/abs/2501.16962v1
- Date: Tue, 28 Jan 2025 14:05:06 GMT
- Title: UEFI Memory Forensics: A Framework for UEFI Threat Analysis
- Authors: Kalanit Suzan Segal, Hadar Cochavi Gorelik, Oleg Brodt, Yuval Elbahar, Yuval Elovici, Asaf Shabtai,
- Abstract summary: We introduce a framework for UEFI memory forensics.
The proposed framework consists of two primary components: UefiMemDump, a memory acquisition tool, and UEFIDumpAnalysis, an extendable collection of analysis modules.
Our work enables researchers and practitioners to investigate firmware-level threats, develop additional analysis modules, and advance overall below-OS security.
- Score: 22.944352324963546
- License:
- Abstract: Modern computing systems rely on the Unified Extensible Firmware Interface (UEFI), which has replaced the traditional BIOS as the firmware standard for the modern boot process. Despite the advancements, UEFI is increasingly targeted by threat actors seeking to exploit its execution environment and take advantage of its persistence mechanisms. While some security-related analysis of UEFI components has been performed--primarily via debugging and runtime behavior testing--to the best of our knowledge, no prior study has specifically addressed capturing and analyzing volatile UEFI runtime memory to detect malicious exploitation during the pre-OS phase. This gap in UEFI forensic tools limits the ability to conduct in-depth security analyses in pre-OS environments. Such a gap is especially surprising, given that memory forensics is widely regarded as foundational to modern incident response, reflected by the popularity of above-OS memory analysis frameworks, such as Rekall, Volatility, and MemProcFS. To address the lack of below-OS memory forensics, we introduce a framework for UEFI memory forensics. The proposed framework consists of two primary components: UefiMemDump, a memory acquisition tool, and UEFIDumpAnalysis, an extendable collection of analysis modules capable of detecting malicious activities such as function pointer hooking, inline hooking, and malicious image loading. Our proof-of-concept implementation demonstrates our framework's ability to detect modern UEFI threats, such as ThunderStrike, CosmicStrand, and Glupteba bootkits. By providing an open-source solution, our work enables researchers and practitioners to investigate firmware-level threats, develop additional analysis modules, and advance overall below-OS security through UEFI memory analysis.
Related papers
- Uncovering EDK2 Firmware Flaws: Insights from Code Audit Tools [1.2713814898630649]
General code audit tools for firmware analysis proven effective in identifying critical areas for enhancement in firmware security.
UEFI Development Kit II (EDK2) plays a crucial role in shaping firmware architecture.
scarcity of open-source tools specifically designed for firmware analysis emphasizes the need for adaptable, innovative solutions.
arXiv Detail & Related papers (2024-09-22T12:29:28Z) - UEFI Vulnerability Signature Generation using Static and Symbolic Analysis [2.6111533042510673]
We introduce a technique called STatic Analysis guided Symbolic Execution (STASE)
STASE integrates both analysis approaches to leverage their strengths and minimize their weaknesses.
It detects and generates vulnerability signatures for 5 out of 9 recently reported PixieFail vulnerabilities and 13 new vulnerabilities in Tianocore's EDKII.
arXiv Detail & Related papers (2024-07-09T18:08:49Z) - A Survey of Unikernel Security: Insights and Trends from a Quantitative Analysis [0.0]
This research presents a quantitative methodology using TF-IDF to analyze the focus of security discussions within unikernel research literature.
Memory Protection Extensions and Data Execution Prevention were the least frequently occurring topics, while SGX was the most frequent topic.
arXiv Detail & Related papers (2024-06-04T00:51:12Z) - JITScanner: Just-in-Time Executable Page Check in the Linux Operating System [6.725792100548271]
JITScanner is developed as a Linux-oriented package built upon a Loadable Kernel Module (LKM)
It integrates a user-level component that communicates efficiently with the LKM using scalable multi-processor/core technology.
JITScanner's effectiveness in detecting malware programs and its minimal intrusion in normal runtime scenarios have been extensively tested.
arXiv Detail & Related papers (2024-04-25T17:00:08Z) - 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) - Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector [72.05791402494727]
This paper studies the challenging cross-domain few-shot object detection (CD-FSOD)
It aims to develop an accurate object detector for novel domains with minimal labeled examples.
arXiv Detail & Related papers (2024-02-05T15:25:32Z) - SoK: Security Below the OS -- A Security Analysis of UEFI [27.91463285974765]
We study a spectrum of UEFI-targeted attacks and proofs of concept (PoCs) for exploiting UEFI-related vulnerabilities.
We present a MITRE ATT&CK-like taxonomy delineating tactics, techniques, and sub-techniques in the context of UEFI attacks.
This paper seeks to clarify the complexities of UEFI and equip the cybersecurity community with the necessary knowledge to strengthen the security of this critical component against a growing threat landscape.
arXiv Detail & Related papers (2023-11-07T08:45:39Z) - Analysis of the Memorization and Generalization Capabilities of AI
Agents: Are Continual Learners Robust? [91.682459306359]
In continual learning (CL), an AI agent learns from non-stationary data streams under dynamic environments.
In this paper, a novel CL framework is proposed to achieve robust generalization to dynamic environments while retaining past knowledge.
The generalization and memorization performance of the proposed framework are theoretically analyzed.
arXiv Detail & Related papers (2023-09-18T21:00:01Z) - DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified
Robustness [58.23214712926585]
We develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection.
Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables.
We are the first to offer certified robustness in the realm of static detection of malware executables.
arXiv Detail & Related papers (2023-03-20T17:25:22Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - Adversarial EXEmples: A Survey and Experimental Evaluation of Practical
Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes.
We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks.
These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
arXiv Detail & Related papers (2020-08-17T07:16:57Z)
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.