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
Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

Information Gain Filtration Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF sho

4 Jul 28, 2022
Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent

Narya The Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent. This repository

Paul Garnier 121 Dec 30, 2022
This repo contains implementation of different architectures for emotion recognition in conversations.

Emotion Recognition in Conversations Updates πŸ”₯ πŸ”₯ πŸ”₯ Date Announcements 03/08/2021 πŸŽ† πŸŽ† We have released a new dataset M2H2: A Multimodal Multiparty

Deep Cognition and Language Research (DeCLaRe) Lab 1k Dec 30, 2022
PyTorch implementation for OCT-GAN Neural ODE-based Conditional Tabular GANs (WWW 2021)

OCT-GAN: Neural ODE-based Conditional Tabular GANs (OCT-GAN) Code for reproducing the experiments in the paper: Jayoung Kim*, Jinsung Jeon*, Jaehoon L

BigDyL 7 Dec 27, 2022
This program creates a formatted excel file which highlights the undervalued stock according to Graham's number.

Over-and-Undervalued-Stocks Of Nepse Using Graham's Number Scrap the latest data using different websites and creates a formatted excel file that high

6 May 03, 2022
dualFace: Two-Stage Drawing Guidance for Freehand Portrait Sketching (CVMJ)

dualFace dualFace: Two-Stage Drawing Guidance for Freehand Portrait Sketching (CVMJ) We provide python implementations for our CVM 2021 paper "dualFac

Haoran XIE 46 Nov 10, 2022
Official repository for "PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation"

pair-emnlp2020 Official repository for the paper: Xinyu Hua and Lu Wang: PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long

Xinyu Hua 31 Oct 13, 2022
Repository for the NeurIPS 2021 paper: "Exploiting Domain-Specific Features to Enhance Domain Generalization".

meta-Domain Specific-Domain Invariant (mDSDI) Source code implementation for the paper: Manh-Ha Bui, Toan Tran, Anh Tuan Tran, Dinh Phung. "Exploiting

VinAI Research 12 Nov 25, 2022
γ€ŠK-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters》(2020)

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters This repository is the implementation of the paper "K-Adapter: Infusing Knowledge

Microsoft 118 Dec 13, 2022
Differentiable molecular simulation of proteins with a coarse-grained potential

Differentiable molecular simulation of proteins with a coarse-grained potential This repository contains the learned potential, simulation scripts and

UCL Bioinformatics Group 44 Dec 10, 2022
Opinionated code formatter, just like Python's black code formatter but for Beancount

beancount-black Opinionated code formatter, just like Python's black code formatter but for Beancount Try it out online here Features MIT licensed - b

Launch Platform 16 Oct 11, 2022
Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'

PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urb

Yu Tian 117 Jan 03, 2023
10th place solution for Google Smartphone Decimeter Challenge at kaggle.

Under refactoring 10th place solution for Google Smartphone Decimeter Challenge at kaggle. Google Smartphone Decimeter Challenge Global Navigation Sat

12 Oct 25, 2022
πŸ”Ž Monitor deep learning model training and hardware usage from your mobile phone πŸ“±

Monitor deep learning model training and hardware usage from mobile. πŸ”₯ Features Monitor running experiments from mobile phone (or laptop) Monitor har

labml.ai 1.2k Dec 25, 2022
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In

Trieu 6.1k Dec 30, 2022
Multi-Scale Aligned Distillation for Low-Resolution Detection (CVPR2021)

MSAD Multi-Scale Aligned Distillation for Low-Resolution Detection Lu Qi*, Jason Kuen*, Jiuxiang Gu, Zhe Lin, Yi Wang, Yukang Chen, Yanwei Li, Jiaya J

DV Lab 115 Dec 23, 2022
[arXiv22] Disentangled Representation Learning for Text-Video Retrieval

Disentangled Representation Learning for Text-Video Retrieval This is a PyTorch implementation of the paper Disentangled Representation Learning for T

Qiang Wang 49 Dec 18, 2022
Offical code for the paper: "Growing 3D Artefacts and Functional Machines with Neural Cellular Automata" https://arxiv.org/abs/2103.08737

Growing 3D Artefacts and Functional Machines with Neural Cellular Automata Video of more results: https://www.youtube.com/watch?v=-EzztzKoPeo Requirem

Robotics Evolution and Art Lab 51 Jan 01, 2023
Source code of NeurIPS 2021 Paper ''Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration''

CaGCN This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration". Paper L

6 Dec 19, 2022
Applying PVT to Semantic Segmentation

Applying PVT to Semantic Segmentation Here, we take MMSegmentation v0.13.0 as an example, applying PVTv2 to SemanticFPN. For details see Pyramid Visio

35 Nov 30, 2022