Angora is a mutation-based fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution.

Last update: Jan 07, 2023

Overview

Angora

Angora is a mutation-based coverage guided fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution.

Published Work

Arxiv: Angora: Efficient Fuzzing by Principled Search, S&P 2018.

Building Angora

Build Requirements

Linux-amd64 (Tested on Ubuntu 16.04/18.04 and Debian Buster)
Rust stable (>= 1.31), can be obtained using rustup
LLVM 4.0.0 - 7.1.0 : run PREFIX=/path-to-install ./build/install_llvm.sh.

Environment Variables

Append the following entries in the shell configuration file (~/.bashrc, ~/.zshrc).

export PATH=/path-to-clang/bin:$PATH
export LD_LIBRARY_PATH=/path-to-clang/lib:$LD_LIBRARY_PATH

Fuzzer Compilation

The build script will resolve most dependencies and setup the runtime environment.

./build/build.sh

System Configuration

As with AFL, system core dumps must be disabled.

echo core | sudo tee /proc/sys/kernel/core_pattern

Test

Test if Angora is builded successfully.

cd /path-to-angora/tests
./test.sh mini

Running Angora

Build Target Program

Angora compiles the program into two separate binaries, each with their respective instrumentation. Using autoconf programs as an example, here are the steps required.

# Use the instrumenting compilers
CC=/path/to/angora/bin/angora-clang \
CXX=/path/to/angora/bin/angora-clang++ \
LD=/path/to/angora/bin/angora-clang \
PREFIX=/path/to/target/directory \
./configure --disable-shared

# Build with taint tracking support 
USE_TRACK=1 make -j
make install

# Save the compiled target binary into a new directory
# and rename it with .taint postfix, such as uniq.taint

# Build with light instrumentation support
make clean
USE_FAST=1 make -j
make install

# Save the compiled binary into the directory previously
# created and rename it with .fast postfix, such as uniq.fast

If you fail to build by this approach, try wllvm and gllvm described in Build a target program.

Also, we have implemented taint analysis with libdft64 instead of DFSan (Use libdft64 for taint tracking).

Fuzzing

./angora_fuzzer -i input -o output -t path/to/taint/program -- path/to/fast/program [argv]

For more information, please refer to the documentation under the docs/ directory.

Comments

Unable to compile lavam programs correctly
Hello Angora authors,

I'm trying to reproduce the lavam evaluation within Magma's infrastructure. However, I think I encounter the following 2 issues. Could you help me to check if I'm doing anything wrong?

Thank you in advance!

The 2 issues are as follow:

Angora cannot find any bugs while AFLplusplus can easily discover ones within a few minutes. From the log files I see that Angora is saying Multiple inconsistent warnings. It caused by the fast and track programs has different behaviors. If most constraints are inconsistent, ensure they are compiled with the same environment. Otherwise, please report us.

For who, AFLplusplus can only find <20 bugs after running for 5 hours. For other targets it is finding the numbers of bugs reported in your paper.

You can find the scripts I use to compile and run the fuzzing campaigns here. Basically, the lavam programs are compiled with fuzzers/aflplusplus/instrument.sh and fuzzers/angora/instrument.sh, which they set up some config and execute targets/lavam/build.sh.
In targets/lavam/LAVAM you can find the patched source code following your instructions.

To launch the fuzzing campaigns, cd into tools/captain and run ./run.sh run_lavamrc.
run_lavamrc is the config file for the campaign. It would create a working directory in ~/lavam-results, build docker containers and start fuzzing with fuzzers/aflplusplus/run.sh and fuzzers/angora/run.sh. The fuzzing results are stored in ~/lavam-results/ar as tarballs.

Please do let me know if you need any additional information.

Spencer
opened by spencerwuwu 1
Fix up compiler warnings
Correct signedness for c-strings in angora-clang

Const-correctness throughout

Move #[link] attribute to extern block

Fixes all warnings emitted by clang version 14.
opened by bossmc 0
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dependencies

opened by dependabot-preview[bot] 1

Angora compile IR

Would Angora have support to compile from LLVM or BAP derived intermediate representation?

Trying to analyze binary (pre-compiled) but couldn't figure out how:

 INFO  angora::fuzz_main > CommandOpt { mode: LLVM, id: 0, main: ("/input/azorult2", []), track: ("/input/azorult2", []), tmp_dir: "./output/bar/tmp", out_file: "./output/bar/tmp/cur_input", forksrv_socket_path: "./output/bar/tmp/forksrv_socket", track_path: "./output/bar/tmp/track", is_stdin: true, search_method: Gd, mem_limit: 200, time_limit: 1, is_raw: true, uses_asan: false, ld_library: "$LD_LIBRARY_PATH:/clang+llvm/lib", enable_afl: true, enable_exploitation: true }
thread 'main' panicked at 'The program is not complied by Angora', fuzzer/src/check_dep.rs:55:9

opened by aug2uag 1

Update rand requirement from 0.7 to 0.8
Updates the requirements on rand to permit the latest version.

Changelog

Sourced from rand's changelog.

[0.8.0] - 2020-12-18

Platform support

The minimum supported Rust version is now 1.36 (#1011)

getrandom updated to v0.2 (#1041)

Remove wasm-bindgen and stdweb feature flags. For details of WASM support, see the getrandom documentation. (#948)

ReadRng::next_u32 and next_u64 now use little-Endian conversion instead of native-Endian, affecting results on Big-Endian platforms (#1061)

The nightly feature no longer implies the simd_support feature (#1048)

Fix simd_support feature to work on current nightlies (#1056)

Rngs

ThreadRng is no longer Copy to enable safe usage within thread-local destructors (#1035)

gen_range(a, b) was replaced with gen_range(a..b). gen_range(a..=b) is also supported. Note that a and b can no longer be references or SIMD types. (#744, #1003)

Replace AsByteSliceMut with Fill and add support for [bool], [char], [f32], [f64] (#940)

Restrict rand::rngs::adapter to std (#1027; see also #928)

StdRng: add new std_rng feature flag (enabled by default, but might need to be used if disabling default crate features) (#948)

StdRng: Switch from ChaCha20 to ChaCha12 for better performance (#1028)

SmallRng: Replace PCG algorithm with xoshiro{128,256}++ (#1038)

Sequences

Add IteratorRandom::choose_stable as an alternative to choose which does not depend on size hints (#1057)

Improve accuracy and performance of IteratorRandom::choose (#1059)

Implement IntoIterator for IndexVec, replacing the into_iter method (#1007)

Add value stability tests for seq module (#933)

Misc

Support PartialEq and Eq for StdRng, SmallRng and StepRng (#979)

Added a serde1 feature and added Serialize/Deserialize to UniformInt and WeightedIndex (#974)

Drop some unsafe code (#962, #963, #1011)

Reduce packaged crate size (#983)

Migrate to GitHub Actions from Travis+AppVeyor (#1073)

Distributions

Alphanumeric samples bytes instead of chars (#935)

Uniform now supports char, enabling rng.gen_range('A'..='Z') (#1068)

Add UniformSampler::sample_single_inclusive (#1003)

Weighted sampling

Implement weighted sampling without replacement (#976, #1013)

rand::distributions::alias_method::WeightedIndex was moved to rand_distr::WeightedAliasIndex. The simpler alternative rand::distribution::WeightedIndex remains. (#945)

Improve treatment of rounding errors in WeightedIndex::update_weights (#956)

WeightedIndex: return error on NaN instead of panic (#1005)

Documentation

Document types supported by random (#994)

Commits

98a1aaf Merge pull request #1076 from dhardy/work

46082d5 Fix installation of multilib

d109f18 Update changelog for rand v0.8

cc93dae Merge pull request #1079 from JJPennington/master

9aa8bc1 Modified the StdRng documentation

34423a6 Use cp to create redirect

d4b180d Merge pull request #1078 from rust-random/work2

1959b1a Adjust homepage to point at the book

e9adf45 Switch documentation links to docs.rs

da3458b Add HTML redirect

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dependencies
opened by dependabot-preview[bot] 0
showmap: added tool for displaying coverage data

Analogous to afl-showmap. Logs code coverage information to a file (in the same format as afl-showmap).

This is my first time writing Rust, so I hope that it's okay!

opened by adrianherrera 0

Releases(1.3.0)

1.3.0(Apr 13, 2022)
Support LLVM 11/12

Tested in Rust 1.6.*, and Ubuntu 20.04

Fix issues

getc model

https://github.com/AngoraFuzzer/Angora/commit/b31af93bb7401a296af0ddaa7b80eaaed7f73415

https://github.com/AngoraFuzzer/Angora/issues/86

New PRs

Source code(tar.gz)
Source code(zip)
1.2.2(Jul 17, 2019)
Implementation of Never-zero counter: The idea is from Marc and Heiko in AFLPlusPlus . https://github.com/vanhauser-thc/AFLplusplus/blob/master/llvm_mode/README.neverzero

add inst_ratio : issue #67

fix asan compatible: did not instrument function startswith "asan.module"

Source code(tar.gz)
Source code(zip)
1.2.1(Jun 14, 2019)
Use Cmake to compile in llvm_mode

Improve custom function context

Source code(tar.gz)
Source code(zip)
1.2.0(May 23, 2019)
Support Intel Pin && libdft64 for tracking

Support LLVM4-LLVM7

Fix bugs

Improve infer size

improve function call level context ...

Source code(tar.gz)
Source code(zip)

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Angora is a mutation-based fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution.

Related tags

Overview

Angora

Published Work

Building Angora

Build Requirements

Environment Variables

Fuzzer Compilation

System Configuration

Test

Running Angora

Build Target Program

Fuzzing

Comments

Unable to compile lavam programs correctly

Fix up compiler warnings

Upgrade to GitHub-native Dependabot

Angora compile IR

Update rand requirement from 0.7 to 0.8

[0.8.0] - 2020-12-18

Platform support

Rngs

Sequences

Misc

Distributions

Weighted sampling

Documentation

showmap: added tool for displaying coverage data

Releases(1.3.0)

1.3.0(Apr 13, 2022)

1.2.2(Jul 17, 2019)

1.2.1(Jun 14, 2019)

1.2.0(May 23, 2019)

Owner

This is a TensorFlow implementation for C2-Rec

PyTorch implementation of "Dataset Knowledge Transfer for Class-Incremental Learning Without Memory" (WACV2022)

A practical ML pipeline for data labeling with experiment tracking using DVC.

An easy-to-use app to visualise attentions of various VQA models.

Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)

torchbearer: A model fitting library for PyTorch

Code release for "BoxeR: Box-Attention for 2D and 3D Transformers"

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

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