and blind attack models. TAADPapers (https://github.com/thunlp/TAADpapers) is a paper list which summarizes almost all the papers concerning textual adversarial attack and defense. You can have a look at this list to find more attack models. Currently OpenAttack includes 13 typical attack models against text classification models that cover all attack types. Here is the list of currently involved attack models. Sentence-level (SEA) Semantically Equivalent Adversarial Rules for Debugging (https://www.kitploit.com/search/label/Debugging) NLP Models. Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin. ACL 2018. decision [pdf (https://aclweb.org/anthology/P18-1079)] [code (https://github.com/marcotcr/sears)] (SCPN) Adversarial Example Generation with Syntactically Controlled Paraphrase Networks. Mohit Iyyer, John Wieting, Kevin Gimpel, Luke Zettlemoyer. NAACL-HLT 2018. blind [pdf (https://www.aclweb.org/anthology/N18-1170)] [code&data (https://github.com/miyyer/scpn)] (GAN) Generating Natural Adversarial Examples. Zhengli Zhao, Dheeru Dua, Sameer Singh. ICLR 2018. decision [pdf (https://arxiv.org/pdf/1710.11342.pdf)] [code (https://github.com/zhengliz/natural-adversary)] Word-level (SememePSO) Word-level Textual Adversarial Attacking as Combinatorial Optimization. Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu and Maosong Sun. ACL 2020. score [pdf (https://www.aclweb.org/anthology/2020.acl-main.540.pdf)] [code (https://github.com/thunlp/SememePSO-Attack)] (TextFooler) Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment. Di Jin, Zhijing Jin, Joey Tianyi Zhou, Peter Szolovits. AAAI-20. score [pdf (https://arxiv.org/pdf/1907.11932v4)] [code (https://github.com/wqj111186/TextFooler)] (PWWS) Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency. Shuhuai Ren, Yihe Deng, Kun He, Wanxiang Che. ACL 2019. score [pdf (https://www.aclweb.org/anthology/P19-1103.pdf)] [code (https://github.com/JHL-HUST/PWWS/)] (Genetic) Generating Natural Language Adversarial Examples. Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, Kai-Wei Chang. EMNLP 2018. score [pdf (https://www.aclweb.org/anthology/D18-1316)] [code (https://github.com/nesl/nlp_adversarial_examples)] (FD) Crafting Adversarial Input Sequences For Recurrent Neural Networks. Nicolas Papernot, Patrick McDaniel, Ananthram Swami, Richard Harang. MILCOM 2016. gradient [pdf (https://arxiv.org/pdf/1604.08275.pdf)] Word/Char-level (UAT) Universal Adversarial Triggers for Attacking and Analyzing NLP. Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh. EMNLP-IJCNLP 2019. gradient [pdf (https://arxiv.org/pdf/1908.07125.pdf)] [code (https://github.com/Eric-Wallace/universal-triggers)] [website (http://www.ericswallace.com/triggers)] (TextBugger) TEXTBUGGER: Generating Adversarial Text Against Real-world Applications. Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, Ting Wang. NDSS 2019. gradient score [pdf (https://arxiv.org/pdf/1812.05271.pdf)] (HotFlip) HotFlip: White-Box Adversarial Examples for Text Classification. Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou. ACL 2018. gradient [pdf (https://www.aclweb.org/anthology/P18-2006)] [code (https://github.com/AnyiRao/WordAdver)] Char-level (VIPER) Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems. Steffen Eger, Gözde Gül ¸Sahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych. NAACL-HLT 2019. score [pdf (https://www.aclweb.org/anthology/N19-1165)] [code&data (https://github.com/UKPLab/naacl2019-like-humans-visual-attacks)] (DeepWordBug) Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers. Ji Gao, Jack Lanchantin, Mary Lou Soffa, Yanjun Qi. IEEE SPW 2018. score [pdf (https://ieeexplore.ieee.org/document/8424632)] [code

(https://github.com/QData/deepWordBug)] Following table illustrates the comparison of the attack models. Model Accessibility Perturbation Main Idea SEA Decision Sentence Rule-based paraphrasing SCPN Blind Sentence Paraphrasing GAN Decision Sentence Text generation by encoder-decoder SememePSO Score Word Particle Swarm Optimization-based word substitution TextFooler Score Word Greedy word substitution PWWS Score Word Greedy word substitution Genetic Score Word Genetic algorithm-based word substitution FD Gradient Word Gradient-based word substitution TextBugger Gradient, Score Word+Char Greedy word substitution and character manipulation UAT Gradient Word, Char Gradient-based word or character manipulation HotFlip Gradient Word, Char Gradient-based word or character substitution VIPER Blind Char Visually similar character substitution DeepWordBug Score Char Greedy character manipulation
Toolkit Design
Considering the significant distinctions among different attack models, we leave considerable freedom for the skeleton design of attack models, and focus more on streamlining the general processing of adversarial attacking and the common components used in attack models. OpenAttack has 7 main modules:

TextProcessor: processing the original text sequence so as to assist attack models in generating adversarial examples. Classifier: wrapping victim classification models Attacker: involving various attack models Substitute: packing different word/character substitution methods which are widely used in word- and character-level attack models. Metric: providing several adversarial example quality metrics which can serve as either the constraints on the adversarial examples during attacking or evaluation metrics for evaluating adversarial attacks. AttackEval: evaluating textual adversarial attacks from attack effectiveness, adversarial example quality and attack efficiency. DataManager: managing all the data as well as saved models that are used in other modules

Download OpenAttack (https://github.com/thunlp/OpenAttack)

Accidental Bounty Part 2
https://darkshooter.medium.com/accidental-bounty-part-2-8da2539da503?source=rss------bug_bounty-5

Not a funny title, I know…😆😆. Well, we’ll be continuing the story of previous blog today. If you haven’t read it yet, please visit…Continue reading on Medium » (https://darkshooter.medium.com/accidental-bounty-part-2-8da2539da503?source=rss------bug_bounty-5)

hacking: security in practice
Internet connection stripped of context

I hope it's not OT but I didnt know where to ask.

Why Internet Connection is stripped of context?

submitted by /u/daredavid9
[link] [comments]

hacking: security in practice
Lost password

I need help logging back into my roblox acc it has no email but I forgot the password

submitted by /u/Snoo_47772
[link] [comments]

hacking: security in practice
Four Digit Number Generator with Input

I wanted to know If there is a four Digit Number Generator that can Input random four Digit Numbers using my Keyboard (so If I am in Google search bar and activate the Programm it Starts spamming Numbers ) and then when I Cracked my Thing Show me the number

submitted by /u/redditOnell
[link] [comments]

hacking: security in practice
Is it true that if you use an HTTP website everyone can see what you type in the website ?

Or you need specific things like being connected to a compromised router or having malware ?

submitted by /u/AstramaLincroyable
[link] [comments]

5 Most Effective Ways of Learning in Bug Bounty

Follow these ways and no one can stop you from achieving your dream !!
Read more...

Intigriti — XSS Challenge 0621

XSS via WebAssembly
Read more...

Accidental Bounty Part 2

Not a funny title, I know…😆😆. Well, we’ll be continuing the story of previous blog today. If you haven’t read it yet, please visit…Continue reading on Medium »
Read more...

5 Most Effective Ways of Learning in Bug Bounty

Follow these ways and no one can stop you from achieving your dream !!Continue reading on InfoSec Write-ups »
Read more...

Dark Reading: Attacks/Breaches
Stop Playing Catchup: Move From Reactive to Proactive to Defeat Cyber Threats

One-time reactive measures can't keep up. It's time to be proactive and pick our swords and not just our shields.

5 Most Effective Ways of Learning in Bug Bounty
https://infosecwriteups.com/5-most-effective-ways-of-learning-in-bug-bounty-afaf097f4df1?source=rss------bug_bounty-5

Follow these ways and no one can stop you from achieving your dream !!Continue reading on InfoSec Write-ups » (https://infosecwriteups.com/5-most-effective-ways-of-learning-in-bug-bounty-afaf097f4df1?source=rss------bug_bounty-5)

Hacking on Medium
TryHackMe: COUCH CTF — Beginner Friendly (detailed)

https://cdn-images-1.medium.com/max/605/1*n8jPpKhUaKylz4W0j-m-cA.png
PLATFORM: TryHackMe

Continue reading on Medium »