Retrofitting XoM for Stripped Binaries without Embedded Data Relocation
- URL: http://arxiv.org/abs/2412.02110v2
- Date: Wed, 04 Dec 2024 02:47:40 GMT
- Title: Retrofitting XoM for Stripped Binaries without Embedded Data Relocation
- Authors: Chenke Luo, Jiang Ming, Mengfei Xie, Guojun Peng, Jianming Fu,
- Abstract summary: We present PXoM, a practical technique to seamlessly retrofit XoM into stripped binaries on the x86-64 platform.<n>We leverage Intel's hardware feature, Memory Protection Keys, to offer an efficient fine-grained permission control.<n> PXoM leaves adversaries with little wiggle room to harvest all of the required gadgets, suggesting PXoM is practical for real-world deployment.
- Score: 10.947944442975697
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In this paper, we present PXoM, a practical technique to seamlessly retrofit XoM into stripped binaries on the x86-64 platform. As handling the mixture of code and data is a well-known challenge for XoM, most existing methods require the strict separation of code and data areas via either compile-time transformation or binary patching, so that the unreadable permission can be safely enforced at the granularity of memory pages. In contrast to previous approaches, we provide a fine-grained memory permission control mechanism to restrict the read permission of code while allowing legitimate data reads within code pages. This novelty enables PXoM to harden stripped binaries but without resorting to error-prone embedded data relocation. We leverage Intel's hardware feature, Memory Protection Keys, to offer an efficient fine-grained permission control. We measure PXoM's performance with both micro- and macro-benchmarks, and it only introduces negligible runtime overhead. Our security evaluation shows that PXoM leaves adversaries with little wiggle room to harvest all of the required gadgets, suggesting PXoM is practical for real-world deployment.
Related papers
- MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs [82.34547399693966]
Existing methods for lifelong model editing compromise generalization, interfere with past edits, or fail to scale to long editing sequences.<n>We propose MEMOIR, a novel scalable framework that injects knowledge through a residual memory.<n>MeMOIR confines each edit to a distinct subset of the memory parameters, minimizing interference among edits.
arXiv Detail & Related papers (2025-06-09T16:16:42Z) - MOM: Memory-Efficient Offloaded Mini-Sequence Inference for Long Context Language Models [72.61076288351201]
We propose Memory-efficient Offloaded Mini-sequence Inference (MOM)
MOM partitions critical layers into smaller "mini-sequences" and integrates seamlessly with KV cache offloading.
On Meta-Llama-3.2-8B, MOM extends the maximum context length from 155k to 455k tokens on a single A100 80GB GPU.
arXiv Detail & Related papers (2025-04-16T23:15:09Z) - ReF Decompile: Relabeling and Function Call Enhanced Decompile [50.86228893636785]
The goal of decompilation is to convert compiled low-level code (e.g., assembly code) back into high-level programming languages.
This task supports various reverse engineering applications, such as vulnerability identification, malware analysis, and legacy software migration.
arXiv Detail & Related papers (2025-02-17T12:38:57Z) - BitStack: Fine-Grained Size Control for Compressed Large Language Models in Variable Memory Environments [53.71158537264695]
Large language models (LLMs) have revolutionized numerous applications, yet their deployment remains challenged by memory constraints on local devices.
We introduce textbfBitStack, a novel, training-free weight compression approach that enables megabyte-level trade-offs between memory usage and model performance.
arXiv Detail & Related papers (2024-10-31T13:26:11Z) - Memory Scraping Attack on Xilinx FPGAs: Private Data Extraction from Terminated Processes [0.0]
Stratix 10 FPGAs can achieve up to 90% of the performance of a TitanX Pascal GPU while consuming less than 50% of the power.
This makes FPGAs an attractive choice for accelerating machine learning (ML) workloads.
However, our research finds privacy and security vulnerabilities in existing Xilinx FPGA-based hardware acceleration solutions.
arXiv Detail & Related papers (2024-05-22T18:58:20Z) - MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory [49.96019697955383]
We introduce MemLLM, a novel method of enhancing knowledge capabilities by integrating a structured and explicit read-and-write memory module.
Our experiments indicate that MemLLM enhances performance and interpretability, in language modeling general and in particular.
We see MemLLM as an important step towards making LLMs more grounded and factual through memory augmentation.
arXiv Detail & Related papers (2024-04-17T18:13:16Z) - Enabling Memory Safety of C Programs using LLMs [5.297072277460838]
Memory safety violations in low-level code, written in languages like C, continue to remain one of the major sources of software vulnerabilities.
One method of removing such violations by construction is to port C code to a safe C dialect.
Such dialects rely on programmer-supplied annotations to guarantee safety with minimal runtime overhead.
This porting is a manual process that imposes significant burden on the programmer and hence, there has been limited adoption of this technique.
arXiv Detail & Related papers (2024-04-01T13:05:54Z) - Managing Large Enclaves in a Data Center [2.708829957859632]
We propose a new technique, OptMig, to implement secure enclave migration with a near-zero downtime.
Our optimizations reduce the total downtime by 77-96% for a suite of Intel SGX applications that have multi-GB memory footprints.
arXiv Detail & Related papers (2023-11-13T00:08:37Z) - L2MAC: Large Language Model Automatic Computer for Extensive Code Generation [52.81694565226513]
Transformer-based large language models (LLMs) are constrained by the fixed context window of the underlying transformer architecture.
This paper presents L2MAC, the first practical LLM-based general-purpose stored-program automatic computer (von Neumann architecture) framework, for long and consistent output generation.
arXiv Detail & Related papers (2023-10-02T16:55:19Z) - rCanary: Detecting Memory Leaks Across Semi-automated Memory Management Boundary in Rust [4.616001680122352]
Rust is a system programming language that guarantees memory safety via compile-time verifications.
We present rCanary, a static, non-automated, and fully automated model checker to detect leaks across semiautomated boundary.
arXiv Detail & Related papers (2023-08-09T08:26:04Z) - Citadel: Simple Spectre-Safe Isolation For Real-World Programs That Share Memory [8.414722884952525]
We introduce a new security property we call relaxed microarchitectural isolation (RMI)
RMI allows sensitive programs that are not-constant-time to share memory with an attacker while restricting the information leakage to that of non-speculative execution.
Our end-to-end prototype, Citadel, consists of an FPGA-based multicore processor that boots Linux and runs secure applications.
arXiv Detail & Related papers (2023-06-26T17:51:23Z) - XDA: Accurate, Robust Disassembly with Transfer Learning [23.716121748941138]
XDA is a transfer-learning-based disassembly framework.
It learns different contextual dependencies present in machine code.
It is up to 38x faster than hand-written disassemblers like IDA Pro.
arXiv Detail & Related papers (2020-10-02T04:14:17Z) - Sparsifying Parity-Check Matrices [60.28601275219819]
We consider the problem of minimizing the number of one-entries in parity-check matrices.
In the maximum-likelihood (ML) decoding method, the number of ones in PCMs is directly related to the time required to decode messages.
We propose a simple matrix row manipulation which alters the PCM, but not the code itself.
arXiv Detail & Related papers (2020-05-08T05:51:40Z)
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.