Science Score: 57.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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✓DOI references
Found 7 DOI reference(s) in README -
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○Scientific vocabulary similarity
Low similarity (13.4%) to scientific vocabulary
Repository
ROSA: Finding Backdoors with Fuzzing
Basic Info
Statistics
- Stars: 23
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
ROSA: Finding Backdoors with Fuzzing
About
ROSA[^1] is a fuzzing-based toolchain for backdoor detection in binary programs. It uses a state-of-the-art fuzzer (AFL++) coupled with a novel metamorphic oracle to detect many different types of backdoors in different types of binary programs.
Installation
Docker
The recommended way to use ROSA is in a Docker container, to avoid having to build dependencies (such as AFL++).
You can simply pull the existing ROSA Docker image by running:
console
$ docker pull plumtrie/rosa:latest
Then, you can run a container using that image by running:
console
$ docker run -ti --rm -p 4000:4000 plumtrie/rosa:latest
Note that this command will start an interactive session within the container, and that exiting the container will trigger its removal. It will also forward any traffic to port 4000 on the host to port 4000 on the guest, and serve the documentation on that port; this means you can consult the documentation on http://localhost:4000 on the host while the Docker container is running.
Building the Docker image
If you wish to build the Docker image on your machine, you can use the helper build.sh script,
which will automatically tag the image with the current version. See the script itself for more
information.
Before running the script (or simply docker build ...), make sure that you have cloned all of
the submodules used in this repo. You can do this either by cloning the repo with
--recurse-submodules, or by running git submodule update --init --recursive post-cloning.
Be advised that the build might take some time, especially including the time it takes to clone all of the submodules.
Once the Docker image is built, the run.sh convenience script may be used to run it. Generally,
released versions of the image will be tagged, so you can run git checkout <TAG> and run
./build.sh and ./run.sh to build and run a specific version of the image.
Building from source
In order to build ROSA from source, you need the Rust toolchain (specifically Cargo). The recommended way to obtain it is via rustup.
If you also wish to build the documentation, you will need mdBook.
NOTE: ROSA is currently only supported on Linux x86_64 systems. It most definitely depends on libc, so it might not work out of the box (or at all) on other systems.
Before proceeding with the build, make sure that you have cloned all of the submodules used in
this repo. You can do this either by cloning the repo with --recurse-submodules, or by running
git submodule update --init --recursive post-cloning.
Building ROSA
To build ROSA itself, run:
console
$ cargo build --release
To install ROSA, run:
console
$ cargo install --path .
Building AFL++
To build the version of AFL++ that ROSA uses, you first need to install some dependencies. On Debian-based systems, you can run:
console
$ sudo apt install -y build-essential python3-dev automake cmake git flex bison libglib2.0-dev \
libpixman-1-dev python3-setuptools cargo libgtk-3-dev lld llvm llvm-dev \
clang ninja-build cpio libcapstone-dev wget curl python3-pip
$ sudo apt install -y gcc-$(gcc --version|head -n1|sed 's/\..*//'|sed 's/.* //')-plugin-dev \
libstdc++-$(gcc --version|head -n1|sed 's/\..*//'|sed 's/.* //')-dev
You then need to apply some patches to AFL++. In ./fuzzers/aflpp/aflpp/qemu_mode/qemuafl, run:
console
$ patch -p1 < ../../../patches/qemuafl-rosa.patch
Then, in ./fuzzers/aflpp/aflpp, run:
console
$ patch -p1 < ../patches/aflpp-rosa.patch
$ patch -p1 < ../patches/aflpp-qemuafl-build.patch
Finally, in ./fuzzers/aflpp/aflpp, you can build AFL++:
console
$ make -j$(nproc)
And in ./fuzzers/aflpp/aflpp/qemu_mode, you can build QEMU-AFL:
console
$ ./build_qemu_support.sh
Building the documentation
You can build and preview the full documentation with mdbook:
console
$ mdbook serve doc
You can also build and preview the API documentation with cargo doc:
console
$ cargo doc --open
Instructions on how to use and modify ROSA, as well as in-depth explanations of the internals, are available in the documentation.
Contributing
Please read CONTRIBUTING.md.
Citing this repo
When citing the associated ICSE'25 paper, use the following snippet:
bibtex
@inproceedings{kokkonis-2025-rosa,
author = {Kokkonis, Dimitri and Marcozzi, Michaël and Decoux, Emilien and Zacchiroli, Stefano},
booktitle = {2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE)},
title = {ROSA: Finding Backdoors with Fuzzing},
year = {2025},
volume = {},
number = {},
pages = {2816-2828},
keywords = {Runtime;Automation;Manuals;Binary codes;Fuzzing;Benchmark testing;Robustness;Software;Performance analysis;Standards;fuzzing;dynamic analysis;metamorphic testing;backdoors;vulnerability detection},
doi = {10.1109/ICSE55347.2025.00183},
}
When citing the actual repository/tool itself, use CITATION.cff.
[^1]: ROSA is a reference to the song El Paso, but also stands for Runtime trace Oracle-based Selection Algorithm.
Owner
- Name: BINSEC development team
- Login: binsec
- Kind: organization
- Repositories: 6
- Profile: https://github.com/binsec
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: ROSA
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Dimitri
family-names: Kokkonis
email: dimitri.kokkonis@cea.fr
affiliation: 'Université Paris-Saclay, CEA, List'
orcid: 'https://orcid.org/0009-0009-5171-2992'
- given-names: Michaël
family-names: Marcozzi
email: michael.marcozzi@cea.fr
affiliation: 'Université Paris-Saclay, CEA, List'
orcid: 'https://orcid.org/0000-0002-8087-0537'
- given-names: Emilien
family-names: Decoux
email: emilien.decoux@protonmail.com
affiliation: 'Université Paris-Saclay, CEA, List'
- given-names: Stefano
family-names: Zacchiroli
email: stefano.zacchiroli@telecom-paris.fr
affiliation: 'LTCI, Télécom Paris, Institut Polytechnique de Paris'
orcid: 'https://orcid.org/0000-0002-4576-136X'
identifiers:
- type: doi
value: 10.5281/zenodo.14724250
description: Zenodo artifact
- type: swh
value: 'swh:1:rev:d30f7f1800a5dde3b9991125f9b911f8396c6346'
repository-code: 'https://github.com/binsec/rosa'
abstract: >-
A code-level backdoor is a hidden access, programmed and
concealed within the code of a program. For instance,
hard-coded credentials planted in the code of a file
server application would enable maliciously logging into
all deployed instances of this application. Confirmed
software supplychain attacks have led to the injection of
backdoors into popular open-source projects, and backdoors
have been discovered in various router firmware. Manual
code auditing for backdoors is challenging and existing
semi-automated approaches can handle only a limited scope
of programs and backdoors, while requiring manual
reverse-engineering of the audited (binary) program.
Graybox fuzzing (automated semi-randomized testing) has
grown in popularity due to its success in discovering
vulnerabilities and hence stands as a strong candidate for
improved backdoor detection. However, current fuzzing
knowledge does not offer any means to detect the
triggering of a backdoor at runtime. In this work we
introduce ROSA, a novel approach (and tool) which combines
a state-of-the-art fuzzer (AFL++) with a new metamorphic
test oracle, capable of detecting runtime backdoor
triggers. To facilitate the evaluation of ROSA, we have
created ROSARUM, the first openly available benchmark for
assessing the detection of various backdoors in diverse
programs. Experimental evaluation shows that ROSA has a
level of robustness, speed and automation similar to
classical fuzzing. It finds all 17 authentic or synthetic
backdooors from ROSARUM in 1 h 30 on average. Compared to
existing detection tools, it can handle a diversity of
backdoors and programs and it does not rely on manual
reverse-engineering of the fuzzed binary code.
keywords:
- Backdoors
- Fuzzing
- Vulnerability detection
- Binary programs
license: LGPL-2.1-only
GitHub Events
Total
- Issues event: 2
- Watch event: 20
- Issue comment event: 3
- Push event: 14
- Public event: 1
Last Year
- Issues event: 2
- Watch event: 20
- Issue comment event: 3
- Push event: 14
- Public event: 1
Dependencies
- 118 dependencies
- ubuntu 22.04 build