25,752 research outputs found
SAFE: Self-Attentive Function Embeddings for Binary Similarity
The binary similarity problem consists in determining if two functions are
similar by only considering their compiled form. Advanced techniques for binary
similarity recently gained momentum as they can be applied in several fields,
such as copyright disputes, malware analysis, vulnerability detection, etc.,
and thus have an immediate practical impact. Current solutions compare
functions by first transforming their binary code in multi-dimensional vector
representations (embeddings), and then comparing vectors through simple and
efficient geometric operations. However, embeddings are usually derived from
binary code using manual feature extraction, that may fail in considering
important function characteristics, or may consider features that are not
important for the binary similarity problem. In this paper we propose SAFE, a
novel architecture for the embedding of functions based on a self-attentive
neural network. SAFE works directly on disassembled binary functions, does not
require manual feature extraction, is computationally more efficient than
existing solutions (i.e., it does not incur in the computational overhead of
building or manipulating control flow graphs), and is more general as it works
on stripped binaries and on multiple architectures. We report the results from
a quantitative and qualitative analysis that show how SAFE provides a
noticeable performance improvement with respect to previous solutions.
Furthermore, we show how clusters of our embedding vectors are closely related
to the semantic of the implemented algorithms, paving the way for further
interesting applications (e.g. semantic-based binary function search).Comment: Published in International Conference on Detection of Intrusions and
Malware, and Vulnerability Assessment (DIMVA) 201
Lockdown: Dynamic Control-Flow Integrity
Applications written in low-level languages without type or memory safety are
especially prone to memory corruption. Attackers gain code execution
capabilities through such applications despite all currently deployed defenses
by exploiting memory corruption vulnerabilities. Control-Flow Integrity (CFI)
is a promising defense mechanism that restricts open control-flow transfers to
a static set of well-known locations. We present Lockdown, an approach to
dynamic CFI that protects legacy, binary-only executables and libraries.
Lockdown adaptively learns the control-flow graph of a running process using
information from a trusted dynamic loader. The sandbox component of Lockdown
restricts interactions between different shared objects to imported and
exported functions by enforcing fine-grained CFI checks. Our prototype
implementation shows that dynamic CFI results in low performance overhead.Comment: ETH Technical Repor
Mitigating smart card fault injection with link-time code rewriting: a feasibility study
We present a feasibility study to protect smart card software against fault-injection attacks by means of binary code rewriting. We implemented a range of protection techniques in a link-time rewriter and evaluate and discuss the obtained coverage, the associated overhead and engineering effort, as well as its practical usability
Lemon: an MPI parallel I/O library for data encapsulation using LIME
We introduce Lemon, an MPI parallel I/O library that is intended to allow for
efficient parallel I/O of both binary and metadata on massively parallel
architectures. Motivated by the demands of the Lattice Quantum Chromodynamics
community, the data is stored in the SciDAC Lattice QCD Interchange Message
Encapsulation format. This format allows for storing large blocks of binary
data and corresponding metadata in the same file. Even if designed for LQCD
needs, this format might be useful for any application with this type of data
profile. The design, implementation and application of Lemon are described. We
conclude with presenting the excellent scaling properties of Lemon on state of
the art high performance computers
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