Driller: augmenting AFL with symbolic execution!

Related tags

Deep Learningdriller
Overview

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. Driller selectively traces inputs generated by AFL when AFL stops reporting any paths as 'favorites'. Driller will take all untraced paths which exist in AFL's queue and look for basic block transitions AFL failed to find satisfying inputs for. Driller will then use angr to synthesize inputs for these basic block transitions and present it to AFL for syncing. From here, AFL can determine if any paths generated by Driller are interesting, it will then go ahead and mutate these as normal in an attempt to find more paths.

The "Stuck" heuristic

Driller's symbolic execution component is invoked when AFL is 'stuck'. In this implementation, AFL's progress is determined by its 'pending_favs' attribute which can found in the fuzzer_stats file. When this attribute reaches 0, Driller is invoked. Other heuristics could also be used, and it's infact likely that better heuristics exist.

Use in the Cyber Grand Challenge

This same implementation of Driller was used team Shellphish in DARPA's Cyber Grand Challenge (CGC) to aid in the discovery of exploitable bugs. To see how Driller's invokation was scheduled for the CGC you can look at the Mechanical Phish's scheduler component 'meister'.

Current State and Caveats

The code currently supports three modes of operation:

  • A script that facilitates AFL and driller on one machine (over many cores if needed): https://github.com/shellphish/fuzzer/blob/master/shellphuzz
  • A monitor process watches over the fuzzer_stats file to determine when Driller should be invoked. When Driller looks like it could be useful, the monitor process schedules 'jobs' to work over all the inputs AFL has discovered / deemed interesting.
  • Celery tasks are assigned over a fleet of machines, some number of these tasks are assigned to fuzzing, some are assigned to drilling. Fuzzer tasks monitors the stats file, and invokes driller tasks when Driller looks like it could be useful. Redis is used to sync testcases to the filesystem of the fuzzer.

Driller was built and developed for DECREE binaries. While some support for other formats should work out-of-the-box, expect TracerMisfollowErrors to occur when unsupported or incorrectly implemented simprocedures are hit.

Example

Here is an example of using driller to find new testcases based off the trace of a single testcase.

import driller

d = driller.Driller("./CADET_00001",  # path to the target binary
                    "racecar", # initial testcase
                    "\xff" * 65535, # AFL bitmap with no discovered transitions
                   )

new_inputs = d.drill()

Dependencies

  • Mechaphish Fuzzer component
  • Mechaphish Tracer component
Owner
Shellphish
Shellphish
Source code of article "Towards Toxic and Narcotic Medication Detection with Rotated Object Detector"

Towards Toxic and Narcotic Medication Detection with Rotated Object Detector Introduction This is the source code of article: Towards Toxic and Narcot

Woody. Wang 3 Oct 29, 2022
Iran Open Source Hackathon

Iran Open Source Hackathon is an open-source hackathon (duh) with the aim of encouraging participation in open-source contribution amongst Iranian dev

OSS Hackathon 121 Dec 25, 2022
PyTorch GPU implementation of the ES-RNN model for time series forecasting

Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm A GPU-enabled version of the hybrid ES-RNN model by Slawek et al that won the M4 time-series

Kaung 305 Jan 03, 2023
Code for "Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search"

Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search This is an implementation for our paper Contextual Non-Loca

Tencent YouTu Research 50 Dec 03, 2022
SSL_SLAM2: Lightweight 3-D Localization and Mapping for Solid-State LiDAR (mapping and localization separated) ICRA 2021

SSL_SLAM2 Lightweight 3-D Localization and Mapping for Solid-State LiDAR (Intel Realsense L515 as an example) This repo is an extension work of SSL_SL

Wang Han 王晗 1.3k Jan 08, 2023
Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition Introduction Run attack: SGADV.py Objective function: foolbox/attacks/gradi

1 Jul 18, 2022
[ICML 2020] DrRepair: Learning to Repair Programs from Error Messages

DrRepair: Learning to Repair Programs from Error Messages This repo provides the source code & data of our paper: Graph-based, Self-Supervised Program

Michihiro Yasunaga 155 Jan 08, 2023
Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

LEXA Benchmark Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models

Oleg Rybkin 36 Dec 22, 2022
Defending against Model Stealing via Verifying Embedded External Features

Defending against Model Stealing Attacks via Verifying Embedded External Features This is the official implementation of our paper Defending against M

20 Dec 30, 2022
robomimic: A Modular Framework for Robot Learning from Demonstration

robomimic [Homepage]   [Documentation]   [Study Paper]   [Study Website]   [ARISE Initiative] Latest Updates [08/09/2021] v0.1.0: Initial code and pap

ARISE Initiative 178 Jan 05, 2023
Continual World is a benchmark for continual reinforcement learning

Continual World Continual World is a benchmark for continual reinforcement learning. It contains realistic robotic tasks which come from MetaWorld. Th

41 Dec 24, 2022
EssentialMC2 Video Understanding

EssentialMC2 Introduction EssentialMC2 is a complete system to solve video understanding tasks including MHRL(representation learning), MECR2( relatio

Alibaba 106 Dec 11, 2022
The code for the CVPR 2021 paper Neural Deformation Graphs, a novel approach for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects.

Neural Deformation Graphs Project Page | Paper | Video Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction Aljaž Božič, Pablo P

Aljaz Bozic 134 Dec 16, 2022
Style transfer between images was performed using the VGG19 model

Style transfer between images was performed using the VGG19 model. The necessary codes, libraries and all other information of this project are available below

Onur yılmaz 2 May 09, 2022
LiDAR R-CNN: An Efficient and Universal 3D Object Detector

LiDAR R-CNN: An Efficient and Universal 3D Object Detector Introduction This is the official code of LiDAR R-CNN: An Efficient and Universal 3D Object

TuSimple 295 Jan 05, 2023
MARS: Learning Modality-Agnostic Representation for Scalable Cross-media Retrieva

Introduction This is the source code of our TCSVT 2021 paper "MARS: Learning Modality-Agnostic Representation for Scalable Cross-media Retrieval". Ple

7 Aug 24, 2022
Shitty gaze mouse controller

demo.mp4 shitty_gaze_mouse_cotroller install tensofflow, cv2 run the main.py and as it starts it will collect data so first raise your left eyebrow(bo

16 Aug 30, 2022
The trained model and denoising example for paper : Cardiopulmonary Auscultation Enhancement with a Two-Stage Noise Cancellation Approach

The trained model and denoising example for paper : Cardiopulmonary Auscultation Enhancement with a Two-Stage Noise Cancellation Approach

ycj_project 1 Jan 18, 2022
RobustVideoMatting and background composing in one model by using onnxruntime.

RVM_onnx_compose RobustVideoMatting and background composing in one model by using onnxruntime. Usage pip install -r requirements.txt python infer_cam

Quantum Liu 4 Apr 07, 2022
Self-Supervised depth kalilia

Self-Supervised depth kalilia

24 Oct 15, 2022