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
Implementation of "Efficient Regional Memory Network for Video Object Segmentation" (Xie et al., CVPR 2021).

RMNet This repository contains the source code for the paper Efficient Regional Memory Network for Video Object Segmentation. Cite this work @inprocee

Haozhe Xie 76 Dec 14, 2022
DilatedNet in Keras for image segmentation

Keras implementation of DilatedNet for semantic segmentation A native Keras implementation of semantic segmentation according to Multi-Scale Context A

303 Mar 15, 2022
Code for: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification Prerequisite PyTorch = 1.2.0 Python3 torch

16 Dec 14, 2022
[CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation

RCIL [CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation Chang-Bin Zhang1, Jia-Wen Xiao1, Xialei Liu1, Ying-Cong Chen2

Chang-Bin Zhang 71 Dec 28, 2022
Code repo for "FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation" (ICCV 2021)

FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation (ICCV 2021) This repository contains the implementation of th

Yuhang Zang 21 Dec 17, 2022
Learnable Boundary Guided Adversarial Training (ICCV2021)

Learnable Boundary Guided Adversarial Training This repository contains the implementation code for the ICCV2021 paper: Learnable Boundary Guided Adve

DV Lab 27 Sep 25, 2022
[NeurIPS'20] Self-supervised Co-Training for Video Representation Learning. Tengda Han, Weidi Xie, Andrew Zisserman.

CoCLR: Self-supervised Co-Training for Video Representation Learning This repository contains the implementation of: InfoNCE (MoCo on videos) UberNCE

Tengda Han 271 Jan 02, 2023
Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch

Who has never met a forged picture on the web ? No one ! Everyday we are constantly facing fake pictures touched up in Photoshop but it is not always

Rony Abecidan 77 Dec 16, 2022
EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation.

This repository contains data and code for our EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation. Please contact me at

9 Oct 28, 2022
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset (CVPR2022)

FaceVerse FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset Lizhen Wang, Zhiyuan Chen, Tao Yu, Chenguang

Lizhen Wang 219 Dec 28, 2022
Data Consistency for Magnetic Resonance Imaging

Data Consistency for Magnetic Resonance Imaging Data Consistency (DC) is crucial for generalization in multi-modal MRI data and robustness in detectin

Dimitris Karkalousos 19 Dec 12, 2022
[ICCV 2021 Oral] NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo

NerfingMVS Project Page | Paper | Video | Data NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo Yi Wei, Shaohui

Yi Wei 369 Dec 24, 2022
Official Pytorch Implementation of: "Semantic Diversity Learning for Zero-Shot Multi-label Classification"(2021) paper

Semantic Diversity Learning for Zero-Shot Multi-label Classification Paper Official PyTorch Implementation Avi Ben-Cohen, Nadav Zamir, Emanuel Ben Bar

28 Aug 29, 2022
Code for CMaskTrack R-CNN (proposed in Occluded Video Instance Segmentation)

CMaskTrack R-CNN for OVIS This repo serves as the official code release of the CMaskTrack R-CNN model on the Occluded Video Instance Segmentation data

Q . J . Y 61 Nov 25, 2022
A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perform basic tasks.

AI_Personal_Voice_Assistant_Using_Python A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perf

Chumui Tripura 1 Oct 30, 2021
Repositório da disciplina de APC, no segundo semestre de 2021

NOTAS FINAIS: https://github.com/fabiommendes/apc2018/blob/master/nota-final.pdf Algoritmos e Programação de Computadores Este é o Git da disciplina A

16 Dec 16, 2022
Biomarker identification for COVID-19 Severity in BALF cells Single-cell RNA-seq data

scBALF Covid-19 dataset Analysis Here is the Github page that has the codes for the bioinformatics pipeline described in the paper COVID-Datathon: Bio

Nami Niyakan 2 May 21, 2022
Expressive Power of Invariant and Equivaraint Graph Neural Networks (ICLR 2021)

Expressive Power of Invariant and Equivaraint Graph Neural Networks In this repository, we show how to use powerful GNN (2-FGNN) to solve a graph alig

Marc Lelarge 36 Dec 12, 2022
Noether Networks: meta-learning useful conserved quantities

Noether Networks: meta-learning useful conserved quantities This repository contains the code necessary to reproduce experiments from "Noether Network

Dylan Doblar 33 Nov 23, 2022
Official repository of the paper "GPR1200: A Benchmark for General-PurposeContent-Based Image Retrieval"

GPR1200 Dataset GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval (ArXiv) Konstantin Schall, Kai Uwe Barthel, Nico Hezel, Klaus J

Visual Computing Group 16 Nov 21, 2022