A symbolic-model-guided fuzzer for TLS

Overview

tlspuffin

Logo with Penguin

TLS Protocol Under FuzzINg
A symbolic-model-guided fuzzer for TLS

Disclaimer: The term "symbolic-model-guided" should not be confused with symbolic execution or concolic fuzzing.

Description

Fuzzing implementations of cryptographic protocols is challenging. In contrast to traditional fuzzing of file formats, cryptographic protocols require a specific flow of cryptographic and mutually dependent messages to reach deep protocol states. The specification of the TLS protocol describes sound flows of messages and cryptographic operations.

Although the specification has been formally verified multiple times with significant results, a gap has emerged from the fact that implementations of the same protocol have not undergone the same logical analysis. Because the development of cryptographic protocols is error-prone, multiple security vulnerabilities have already been discovered in implementations in TLS which are not present in its specification.

Inspired by symbolic protocol verification, we present a reference implementation of a fuzzer named tlspuffin which employs a concrete semantic to execute TLS 1.2 and 1.3 symbolic traces. In fact attacks which mix \TLS versions are in scope of this implementation. This method allows us to utilize a genetic fuzzing algorithm to fuzz protocol flows, which is described by the following three stages.

  • By mutating traces we can deviate from the specification to test logical flaws.
  • Selection of interesting protocol flows advance the fuzzing procedure.
  • A security violation oracle supervises executions for the absence of vulnerabilities.

The novel approach allows rediscovering known vulnerabilities, which are out-of-scope for classical bit-level fuzzers. This proves that it is capable of reaching critical protocol states. In contrast to the promising methodology no new vulnerabilities were found by tlspuffin. This can can be explained by the fact that the implementation effort of TLS protocol primitives and extensions is high and not all features of the specification have been implemented. Nonetheless, the innovating approach is promising in terms of quickly reaching high edge coverage, expressiveness of executable protocol traces and stable and extensible implementation.

Features

  • Uses the LibAFL fuzzing framework
  • Fuzzer which is inspired by the Dolev-Yao symbolic model used in protocol verification
  • Domain specific mutators for Protocol Fuzzing!
  • Supported Libraries Under Test: OpenSSL 1.0.1f, 1.0.2u, 1.1.1k and LibreSSL 3.3.3
  • Reproducible for each LUT. We use Git submodules to link to forks this are in the tlspuffin organisation
  • 70% Test Coverage
  • Writtin in Rust!

Building

Now, to build the project:

git clone [email protected]/tlspuffin/tlspuffin
git submodule update --init --recursive
cargo build

Running

Fuzz using three clients:

cargo run --bin tlspuffin -- --cores 0-3

Note: After switching the Library Under Test or its version do a clean rebuild (cargo clean). For example when switching from OpenSSL 1.0.1 to 1.1.1.

Testing

cargo test

Command-line Interface

The syntax for the command-line of is:

      tlspuffin [⟨options] [⟨sub-commands⟩]

Global Options

Before we explain each sub-command, we first go over the options in the following.

  • -c, --cores ⟨spec⟩

    This option specifies on which cores the fuzzer should assign its worker processes. It can either be specified as a list by using commas "0,1,2,7" or as a range "0-7". By default, it runs just on core 0.

  • -i, --max-iters ⟨i⟩

    This option allows to bound the amount of iterations the fuzzer does. If omitted, then infinite iterations are done.

  • -p, --port ⟨n⟩

    As specified in [sec:design-multiprocessing] the initial communication between the fuzzer broker and workers happens over TCP/IP. Therefore, the broker requires a port allocation. The default port is 1337.

  • -s, --seed ⟨n⟩

    Defines an initial seed for the prng used for mutations. Note that this does not make the fuzzing deterministic, because of randomness introduced by the multiprocessing (see [sec:design-multiprocessing]).

Sub-commands

Now we will go over the sub-commands execute, plot, experiment, and seed.

  • execute ⟨input⟩

    This sub-command executes a single trace persisted in a file. The path to the file is provided by the ⟨input⟩ argument.

  • plot ⟨input⟩ ⟨format⟩ ⟨output_prefix⟩

    This sub-command plots the trace stored at ⟨input⟩ in the format specified by ⟨format⟩. The created graphics are stored at a path provided by ⟨output_prefix⟩. The option --multiple can be provided to create for each step in the trace a separate file. If the option --tree is given, then only a single graphic which contains all steps is produced.

  • experiment

    This sub-command initiates an experiment. Experiments are stored in a directory named experiments/ in the current working directory. An experiment consists of a directory which contains . The title and description of the experiment can be specified with --title ⟨t⟩ and --description ⟨d⟩ respectively. Both strings are persisted in the metadata of the experiment, together with the current commit hash of , the version and the current date and time.

  • seed

    This sub-command serializes the default seed corpus in a directory named corpus/ in the current working directory. The default corpus is defined in the source code of using the trace dsl.

Rust Setup

Install rustup.

The toolchain will be automatically downloaded when building this project. See ./rust-toolchain.toml for more details about the toolchain.

Make sure that you have the clang compiler installed. Optionally, also install llvm to have additional tools like sancov available. Also make sure that you have the usual tools for building it like make, gcc etc. installed. They may be needed to build OpenSSL.

Advanced Features

Running with ASAN

ASAN_OPTIONS=abort_on_error=1 \
    cargo run --bin tlspuffin --features asan -- --cores 0-3

It is important to enable abort_on_error, else the fuzzer workers fail to restart on crashes.

Generate Corpus Seeds

cargo run --bin tlspuffin -- seed

Plot Symbolic Traces

To plot SVGs do the following:

cargo run --bin tlspuffin -- plot corpus/seed_client_attacker12.trace svg ./plots/seed_client_attacker12

Note: This requires that the dot binary is in on your path. Note: The utility tools/plot-corpus.sh plots a whole directory

Execute a Symbolic Trace (with ASAN)

To analyze crashes you can also execute a trace which crashes the testing harness using ASAN:

cargo run --bin tlspuffin -- execute test.trace

To do the same with ASAN enabled:

ASAN_OPTIONS=detect_leaks=0 \
      cargo run --bin tlspuffin --features asan -- execute test.trace

Crash Deduplication

Creates log files for each crash and parses ASAN crashes to group crashes together.

tools/analyze-crashes.sh

Benchmarking

There is a benchmark which compares the execution of the dynamic functions to directly executing them in benchmark.rs. You can run them using:

cargo bench
xdg-open target/criterion/report/index.html

Documentation

This generates the documentation for this crate and opens the browser. This also includes the documentation of every dependency like LibAFL or rustls.

cargo doc --open

You can also view the up-to-date documentation here.

You might also like...
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

============================================================================================================ `MILA will stop developing Theano https:

ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs
ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

(Comet-) ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs Paper Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sa

PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World [ACL 2021]

piglet PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World [ACL 2021] This repo contains code and data for PIGLeT. If you like

Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis in JAX

SYMPAIS: Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis Overview | Installation | Documentation | Examples | Notebo

Official repository for the paper, MidiBERT-Piano: Large-scale Pre-training for Symbolic Music Understanding.
Official repository for the paper, MidiBERT-Piano: Large-scale Pre-training for Symbolic Music Understanding.

MidiBERT-Piano Authors: Yi-Hui (Sophia) Chou, I-Chun (Bronwin) Chen Introduction This is the official repository for the paper, MidiBERT-Piano: Large-

Source code and Dataset creation for the paper "Neural Symbolic Regression That Scales"

NeuralSymbolicRegressionThatScales Pytorch implementation and pretrained models for the paper "Neural Symbolic Regression That Scales", presented at I

Data and Code for ACL 2021 Paper
Data and Code for ACL 2021 Paper "Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning"

Introduction Code and data for ACL 2021 Paper "Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning". We cons

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning

safe-control-gym Physics-based CartPole and Quadrotor Gym environments (using PyBullet) with symbolic a priori dynamics (using CasADi) for learning-ba

Driller: augmenting AFL with symbolic execution!

Driller Driller is an implementation of the driller paper. This implementation was built on top of AFL with angr being used as a symbolic tracer. Dril

Comments
  • Support for WolfSSL through a new rust-wolfssl crate

    Support for WolfSSL through a new rust-wolfssl crate

    I tried another approach as I struggled with #136 to debug some SEGFAULT.

    Here the approach is to clone rust-openssl, minimize the exposed interface while exposing what we need for wolfssl_binding.rs, then plug in wolfssl-sys instead of openssl-sys.

    opened by LCBH 3
  • Rediscover wolfSSL vulnerabilities

    Rediscover wolfSSL vulnerabilities

    • CVE-2020-12457 in <4.5.0 https://nvd.nist.gov/vuln/detail/CVE-2020-12457 (DDOS against server, needs change_cipher_spec (CCS) message mutations)
    • CVE 2020-24613 in <4.5.0 https://nvd.nist.gov/vuln/detail/CVE-2020-24613 in < 4.5.0, TLS 1.3 server auth. bypass
    • CVE-2021-3336 in < 4.7.0 https://nvd.nist.gov/vuln/detail/CVE-2021-3336, TLS 1.3 server auth. bypass
    • CVE 2022-25638 in < 5.2.0 https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-25638, TLS 1.3 server auth. bypass
    • CVE-2022-25640 in <5.2.0 https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-25640 (attack on client authentication, needs client authentication through cert.)
    opened by maxammann 1
Releases(evaluation)
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval

More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdh

Ayan Kumar Bhunia 22 Aug 27, 2022
This GitHub repository contains code used for plots in NeurIPS 2021 paper 'Stochastic Multi-Armed Bandits with Control Variates.'

About Repository This repository contains code used for plots in NeurIPS 2021 paper 'Stochastic Multi-Armed Bandits with Control Variates.' About Code

Arun Verma 1 Nov 09, 2021
Stochastic Extragradient: General Analysis and Improved Rates

Stochastic Extragradient: General Analysis and Improved Rates This repository is the official implementation of the paper "Stochastic Extragradient: G

Hugo Berard 4 Nov 11, 2022
Computational inteligence project on faces in the wild dataset

Table of Contents The general idea How these scripts work? Loading data Needed modules and global variables Parsing the arrays in dataset Extracting a

tooraj taraz 4 Oct 21, 2022
Constructing interpretable quadratic accuracy predictors to serve as an objective function for an IQCQP problem that represents NAS under latency constraints and solve it with efficient algorithms.

IQNAS: Interpretable Integer Quadratic programming Neural Architecture Search Realistic use of neural networks often requires adhering to multiple con

0 Oct 24, 2021
A Closer Look at Structured Pruning for Neural Network Compression

A Closer Look at Structured Pruning for Neural Network Compression Code used to reproduce experiments in https://arxiv.org/abs/1810.04622. To prune, w

Bayesian and Neural Systems Group 140 Dec 05, 2022
Codeflare - Scale complex AI/ML pipelines anywhere

Scale complex AI/ML pipelines anywhere CodeFlare is a framework to simplify the integration, scaling and acceleration of complex multi-step analytics

CodeFlare 169 Nov 29, 2022
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN.

Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU.

Phil Wang 2.3k Jan 09, 2023
This repository gives an example on how to preprocess the data of the HECKTOR challenge

HECKTOR 2021 challenge This repository gives an example on how to preprocess the data of the HECKTOR challenge. Any other preprocessing is welcomed an

56 Dec 01, 2022
This code is part of the reproducibility package for the SANER 2022 paper "Generating Clarifying Questions for Query Refinement in Source Code Search".

Clarifying Questions for Query Refinement in Source Code Search This code is part of the reproducibility package for the SANER 2022 paper "Generating

Zachary Eberhart 0 Dec 04, 2021
Background-Click Supervision for Temporal Action Localization

Background-Click Supervision for Temporal Action Localization This repository is the official implementation of BackTAL. In this work, we study the te

LeYang 221 Oct 09, 2022
Multivariate Boosted TRee

Multivariate Boosted TRee What is MBTR MBTR is a python package for multivariate boosted tree regressors trained in parameter space. The package can h

SUPSI-DACD-ISAAC 61 Dec 19, 2022
This project generates news headlines using a Long Short-Term Memory (LSTM) neural network.

News Headlines Generator bunnysaini/Generate-Headlines Goal This project aims to generate news headlines using a Long Short-Term Memory (LSTM) neural

Bunny Saini 1 Jan 24, 2022
Asterisk is a framework to generate high-quality training datasets at scale

Asterisk is a framework to generate high-quality training datasets at scale

Mona Nashaat 44 Apr 25, 2022
Neural Surface Maps

Neural Surface Maps Official implementation of Neural Surface Maps - Luca Morreale, Noam Aigerman, Vladimir Kim, Niloy J. Mitra [Paper] [Project Page]

Luca Morreale 49 Dec 13, 2022
Testing and Estimation of structural breaks in Stata

xtbreak estimating and testing for many known and unknown structural breaks in time series and panel data. For an overview of xtbreak test see xtbreak

Jan Ditzen 13 Jun 19, 2022
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

Visdom A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Python. Overview Concepts Setup Usage API To

FOSSASIA 9.4k Jan 07, 2023
A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

Documentation | External Resources | Research Paper Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The

Benedek Rozemberczki 188 Dec 29, 2022
Gradient Step Denoiser for convergent Plug-and-Play

Source code for the paper "Gradient Step Denoiser for convergent Plug-and-Play"

Samuel Hurault 11 Sep 17, 2022
Official code repository for the publication "Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons"

Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons This repository contains the code to repr

Computational Neuroscience, University of Bern 3 Aug 04, 2022