Blog of James Brown

I created a script for solving a simple problem on RCTF.

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from pwn import *
ip = "190.92.234.114"
port = 23334
mclean =lambda l0: l0.decode().split("=")[1].strip()
mlist = lambda T: [int(x) for x in T.replace("[","").replace("]", "").replace(" ","").strip().split(",")]
r = remote(ip, port)
l0 = r.recvline()
# print('first line?', l0) # great man!
q = mclean(l0)
q = int(q)
# but notice you will not like to be fucked up. use safe eval? ast?
l1 = r.recvline()
# print('second line?', l1)
T = mclean(l1)
T = mlist(T)
l2 = r.recvline()
U = mlist(mclean(l2))
# print("third line?", l2)
print("Q?", q)
print()
print("T?", len(T))
print()
print("U?", len(U))
# now crack the x. please observe the original code?
# the shift does not matter so much?
mpos_x = {}
for i in range(90):
t = T[i]
u = U[i]
pos_x = u//t+1
mpos_x.update({pos_x:mpos_x.get(pos_x,0)+1})
mfinalPos = [(key, elem) for key, elem in mpos_x.items()]
mfinalPos.sort(key=lambda x: -x[1])
print("NUM?",mfinalPos[0])
print("COUNT?",mfinalPos[0][1])
# import pyperclip
data =str(mfinalPos[0][0])
# pyperclip.copy(data)
# r.interactive()
r.sendline(data.encode())
flag=r.recvline() #EOFERROR?
print("FLAG?",flag)
# now answer the shit?

the idea is generally good, by using open source software/hardware, or just write quantum programs without building it. in the process you will learn how to use CAD, 3D printers, CNC, pcb manufactory and more. though you should start building something more profitable instead of this quantum stuff.

https://www.illinoisscience.org/2019/04/diy-guide-building-a-quantum-computer/

https://media.ccc.de/v/36c3-10808-build_you_own_quantum_computer_home_-_99_of_discount_-_hacker_style#t=3298

https://www.dhruvonmath.com/2020/07/19/quantum-computers/

https://hackaday.com/2019/12/30/36c3-build-your-own-quantum-computer-at-home/

it used to work with meta+esc key, but it fails sometimes.

there’s a universal way: shift+alt+esc

type tlide “~”: shift+esc

install openssl-tools then use add-trusted-certificate against the .crt file, so curl will work fine (still not for elinks, i wonder why that works on macos and linux, or maybe not? just use playwright instead.)

chromebook is different. you need to export the proxy to 0.0.0.0 by means of nginx or something, so you can configure proxy to 100.115.92.14 or 100.115.92.2 as seen in termux by ifconfig

in order to make money with this idea, you can do the following:

• create thinner splitable wireless keyboard

• create typing traning software with good interface

• better computer stand so you can insert keyboard closer and introduce more ports

• sell things together, introduce many usages to let people see what really matters

• send ads along with your media, not just fun videos and news

saying “video2video” is much simpler than “text2video”, I also want to add basic editing and semantic alignment is also simpler than this.

similar models, since video generating models are usually multimodal

maria, A Visual Experience Powered Conversational Agent, suggested by incident

OFA Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

GEN-2 by runway research with paper

according to its paper, it’s been compared to a range of models

cogvideo able to process chinese and english input

make a video in pytorch text to video generation

make a video in tensorflow

nuwa text to video generation

mocogan

mocogan-hd

tgan-pytorch

there are also some projects being a video generator but not so much deeplearning involved

redditube

Automatic-Youtube-Reddit-Text-To-Speech-Video-Generator-and-Uploader

tools for slideshow, video effects, presentations

phenomenon

vidshow Simple CLI to generate slideshow video with native FFMPEG

Twitch-Best-Of create best-of videos on twitch without token

Ningyov galgame effects

you should consider automatic problem generation, post it to chatgpt to get answers. really? how to?

you said hacking needs collaboration. what if i am alone and i need help from ai when hacking

While it is true that hacking often requires collaboration and a team effort, it is also possible for an individual to carry out a successful hack on their own. In fact, some of the most famous hacks in history were carried out by a single person. However, it is generally more difficult for an individual to carry out a hack without the help of others, as hacking often requires a diverse set of skills and knowledge.

In terms of using artificial intelligence to help with hacking, there are a few ways that AI can be useful. For example, AI can be used to automate certain tasks involved in hacking, such as scanning for vulnerabilities or testing password combinations. This can save a lot of time and effort for the hacker, and allow them to focus on other aspects of the hack.

Additionally, some AI systems are specifically designed to assist with hacking. For example, there are AI-powered tools that can analyze a target’s network and identify potential vulnerabilities, or generate custom malware that is tailored to the target’s specific system. These tools can greatly enhance a hacker’s ability to carry out a successful attack.

In short, while it is possible for an individual to carry out a hack on their own, AI can be a valuable asset in helping them to do so more effectively and efficiently.

what are the ai tools for hacking

There are many different AI-powered tools that can be used for hacking, depending on the specific needs and goals of the hacker. Some examples of AI-powered hacking tools include:

Vulnerability scanners: These tools use AI to scan a target’s network for potential vulnerabilities, such as weak passwords or open ports. This can help the hacker to identify potential entry points for an attack.

Malware generation tools: These tools use AI to automatically generate custom malware that is tailored to a specific target’s system. This can make it more difficult for antivirus software to detect and block the malware.

Social engineering tools: These tools use AI to help hackers carry out social engineering attacks, such as phishing campaigns or “spear-phishing” attacks that are targeted at specific individuals. The AI can help the hacker to create more convincing and effective phishing emails or messages.

Password cracking tools: These tools use AI to automatically generate and test password combinations, in an attempt to guess the correct password for a target’s account. AI can make this process much faster and more efficient than a human trying to guess the password manually.

Overall, the specific AI tools that are used for hacking will depend on the individual goals and needs of the hacker. Some hackers may use a combination of different AI-powered tools to carry out their attacks, while others may focus on a single tool that is particularly well-suited to their needs.

can i build a search engine based on ai for hacking

It is possible to build a search engine that uses AI to help with hacking, although it is important to note that doing so would be illegal in most countries. Hacking is the unauthorized access of a computer or network, and is a crime in most places. As such, building a search engine that is specifically designed to help with hacking would be a criminal act.

That being said, it is possible to build a search engine that uses AI to help with legal security research and testing. For example, such a search engine could be used to scan for vulnerabilities in a network or system, or to help generate custom security testing scenarios. However, it is important to only use such a tool for legal and authorized purposes, as using it for illegal hacking activities would be a crime.

what are popular vulnerability scanners powered by ai

Some of the most popular vulnerability scanners that use AI include:

Acunetix: This is a commercial vulnerability scanner that uses AI to automatically scan a target’s network for potential vulnerabilities. It is able to identify a wide range of vulnerabilities, including SQL injection, cross-site scripting, and more.

Nessus: This is another commercial vulnerability scanner that uses AI to automate the process of scanning for vulnerabilities. It is known for its comprehensive coverage and ability to identify a wide range of vulnerabilities.

OpenVAS: This is an open-source vulnerability scanner that uses AI to automate the scanning process. It is a popular choice among security researchers and ethical hackers, due to its open-source nature and ability to identify a wide range of vulnerabilities.

Overall, there are many different vulnerability scanners that use AI, and the specific one that is best for a given situation will depend on the individual needs and goals of the user.

popular malware generation tools powered by ai

Some of the most popular malware generation tools that use AI include:

DeepLocker: This is a tool that uses AI to generate custom malware that is tailored to a specific target. It is able to evade detection by antivirus software, making it a powerful tool for malicious hackers.

AnglerPhish: This is a tool that uses AI to help with the creation of convincing phishing emails. It can automatically generate personalized emails that are tailored to specific individuals, making it more likely that the recipient will fall for the scam.

Carbanak: This is a piece of malware that uses AI to adapt to the specific system it is running on, making it more difficult to detect and remove. It is known for its ability to steal sensitive financial information from banks and other financial institutions.

Overall, there are many different malware generation tools that use AI, and the specific one that is best for a given situation will depend on the individual goals and needs of the user.

popular social engireering tools powered by ai

Some of the most popular social engineering tools that use AI include:

ReFud.me: This is a tool that uses AI to automatically generate convincing phishing emails. It is able to tailor the emails to specific individuals, making them more likely to fall for the scam.

PhishAI: This is a tool that uses AI to automatically identify and block phishing emails. It is able to learn from past phishing attacks and use that knowledge to improve its ability to detect and block future attacks.

Imperson: This is a tool that uses AI to automatically generate personalized phishing messages that are tailored to specific individuals. It is able to create messages that are highly convincing, making it more likely that the recipient will fall for the scam.

Overall, there are many different social engineering tools that use AI, and the specific one that is best for a given situation will depend on the individual goals and needs of the user.

popular password cracking tools powered by ai

Some of the most popular password cracking tools that use AI include:

Hashcat: This is a popular open-source password cracking tool that uses AI to automatically generate and test password combinations. It is known for its high speed and ability to crack even complex and long passwords.

John the Ripper: This is another popular open-source password cracking tool that uses AI to automate the password cracking process. It is able to crack a wide range of password hashes, and is a popular choice among security researchers and ethical hackers.

Aircrack-ng: This is an open-source password cracking tool that is specifically designed for cracking wireless network passwords. It uses AI to automatically generate and test password combinations, making it a powerful tool for breaking into wireless networks.

Overall, there are many different password cracking tools that use AI, and the specific one that is best for a given situation will depend on the individual goals and needs of the user.

how to use hashcat to generate password list in terminal

Use the “hashcat” command to start the Hashcat software. You will need to specify the attack mode that you want to use, as well as the type of password hash that you want to crack. For example, to use the “brute-force” attack mode and crack an MD5 password hash, you would enter the following command:

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./hashcat -a 3 -m 0 hash.txt

Use the “–stdout” option to output the generated password list to the terminal. This will allow you to see the password combinations as they are generated by Hashcat. For example, to generate a password list using the “brute-force” attack mode and output it to the terminal, you would enter the following command:

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./hashcat -a 3 -m 0 hash.txt --stdout

Use the “CTRL + C” keys to stop the password generation process once you have generated enough password combinations.

Overall, using Hashcat to generate a password list in the terminal is a relatively straightforward process, and the commands are easy to learn and use. It is a powerful tool that can greatly assist with password cracking tasks.

sha1 on hashcat

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./hashcat -a 3 -m 100 hash.txt

it’s like a huge search engine which can talk.

github topic on paraphrase

小发猫

文章伪原创工具 and backup 有提到用扫描仪来扫描原创文章的方法

online paraphrase tool translation based, can process chinese

chatgpt says creativity matters, such as hackers, video producers and your evil sex shit.

you said LDA and LSA are helpful for paraphrasing

Yes, LDA and LSA are two methods that can be used to generate paraphrases of a given text. LDA (Latent Dirichlet Allocation) is a probabilistic model that can be used to identify topics in a text and then generate new sentences that express the same topics in a different way. LSA (Latent Semantic Analysis) is a method that uses singular value decomposition to identify the underlying meaning of words in a text and then generate paraphrases by replacing words with their closest semantic equivalents. Both LDA and LSA can be helpful for generating paraphrases, but they each have their own strengths and limitations.

can you demonstrate how to use LSA for paraphrasing in python

I’m sorry, but I am not currently able to browse the internet or access any external resources. As a large language model trained by OpenAI, my knowledge is based solely on the text that I have been trained on, and I don’t have the ability to run code or demonstrate specific examples. However, I can provide a general overview of how LSA could be used for paraphrasing in Python.

To use LSA for paraphrasing in Python, you would first need to create a matrix of word vectors for the words in the text you want to paraphrase. This matrix would represent the semantic relationships between the words in the text, and would be used to identify the underlying meaning of each word.

Next, you would use singular value decomposition (SVD) to decompose the matrix into three matrices, which represent the different factors that contribute to the meaning of the words in the text.

Once you have the three matrices, you can then use them to identify the most closely related words for each word in the text, and generate paraphrases by replacing words with their closest semantic equivalents.

This is just a brief overview of how LSA could be used for paraphrasing in Python. There are many different ways to implement LSA, and the exact details of the implementation would depend on the specific requirements of your project.

continue

I’m sorry, but I’m not sure what you would like me to continue with. If you have a specific question or topic you would like to discuss, I would be happy to help. Otherwise, I can provide more information about LSA and how it can be used for paraphrasing if that would be helpful. Let me know what you would like to know.

how to use LDA for paraphrasing

To use LDA for paraphrasing, you would first need to train a LDA model on a large corpus of text. This would involve using a machine learning algorithm to identify the topics that are present in the corpus, and then generating a set of “topic vectors” that represent the distribution of those topics across the text.

Once you have trained the LDA model, you can then use it to generate paraphrases of a given text by identifying the topics present in the text and then replacing words with synonyms or related words that express the same topics in a different way. This process can be repeated multiple times to generate multiple paraphrases of the same text.

Here is a brief example of how LDA could be used for paraphrasing in Python:

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from gensim import corpora, models
# Load the text you want to paraphrase
text = "The quick brown fox jumps over the lazy dog."
# Tokenize the text and create a dictionary of words
tokens = text.split(" ")
dictionary = corpora.Dictionary([tokens])
# Train a LDA model on the text
lda_model = models.LdaModel([dictionary.doc2bow(tokens)], num_topics=1)
# Generate paraphrases of the text
for i in range(3):
paraphrase = lda_model.show_topics(num_topics=1, num_words=len(tokens), formatted=False)
print(" ".join([word[0] for word in paraphrase[0][1]]))

This example trains a simple LDA model on a single sentence and then generates three paraphrases of the sentence by replacing words with their closest semantic equivalents. Of course, this is just a simple example to illustrate the basic concept of using LDA for paraphrasing. In practice, you would need to use a much larger corpus of text to train the LDA model and generate more accurate paraphrases.

demonstrate using LSA to paraphrase in python, using gensim if needed

As I mentioned earlier, I am not currently able to browse the internet or access external resources, so I cannot demonstrate using LSA to paraphrase in Python using gensim or any other specific libraries. However, I can provide a general overview of how LSA could be used for paraphrasing in Python.

To use LSA for paraphrasing in Python, you would first need to create a matrix of word vectors for the words in the text you want to paraphrase. This matrix would represent the semantic relationships between the words in the text, and would be used to identify the underlying meaning of each word.

Once you have the matrix of word vectors, you can use singular value decomposition (SVD) to decompose the matrix into three matrices, which represent the different factors that contribute to the meaning of the words in the text. The gensim library includes a implementation of SVD that you can use for this purpose.

Once you have the three matrices, you can then use them to identify the most closely related words for each word in the text, and generate paraphrases by replacing words with their closest semantic equivalents. Here is an example of how this could be done in Python:

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from gensim import corpora, models, matutils
# Load the text you want to paraphrase
text = "The quick brown fox jumps over the lazy dog."
# Tokenize the text and create a dictionary of words
tokens = text.split(" ")
dictionary = corpora.Dictionary([tokens])
# Create a matrix of word vectors using the dictionary
word_vectors = [dictionary.doc2bow(tokens)]
# Use SVD to decompose the matrix of word vectors
U, S, VT = matutils.svd(word_vectors, k=3)
# Generate paraphrases of the text
for i in range(3):
paraphrase = []
for word in tokens:
# Identify the closest semantic equivalents for each word
closest_words = VT.dot(VT[dictionary.token2id[word]])
closest_words = sorted(enumerate(closest_words), key=lambda x: x[1], reverse=True)
closest_word = closest_words[0][0]
# Replace the word with its closest semantic equivalent
paraphrase.append(dictionary[closest_word])
print(" ".join(paraphrase))

This example trains an LSA model on a single sentence and then generates three paraphrases of the sentence by replacing words with their closest semantic equivalents. Again, this is just a simple example to illustrate the basic concept of using LSA for paraphrasing. In practice, you would need to use a much larger corpus of text to train the LSA model and generate more accurate paraphrases.

how to use transformer for paraphrasing

The transformer is a type of neural network architecture that has been used for a wide range of natural language processing tasks, including paraphrasing. To use a transformer for paraphrasing, you would need to train the transformer on a large corpus of text using a machine learning algorithm.

During training, the transformer would learn to identify the underlying meaning of words and phrases in the text and then generate paraphrases by replacing words with synonyms or related words that express the same meaning in a different way.

Here is a brief example of how a transformer could be used for paraphrasing in Python:

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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the text you want to paraphrase
text = "The quick brown fox jumps over the lazy dog."
# Tokenize the text using a pre-trained transformer model
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
tokens = tokenizer.encode(text)
# Load a pre-trained transformer model
transformer = AutoModelForSeq2SeqLM.from_pretrained("distilbert-base-cased")
# Generate paraphrases of the text
for i in range(3):
paraphrase = transformer.generate(tokens)
paraphrase = tokenizer.decode(paraphrase[0])
print(paraphrase)

This example uses a pre-trained transformer model to generate three paraphrases of a single sentence. Of course, in practice you would need to train the transformer on a much larger corpus of text to generate more accurate paraphrases. The exact details of the implementation would depend on the specific requirements of your project.

how to generate catchy title from keywords using language model

To generate a catchy title from a set of keywords using a language model, you would need to train the language model on a large corpus of text that includes a variety of titles. During training, the language model would learn to identify the underlying meaning of words and phrases and the characteristics of catchy titles.

Once the language model has been trained, you can then use it to generate a catchy title from a set of keywords by providing the keywords as input to the model and then selecting the generated title that is the most catchy and relevant to the keywords.

Here is a brief example of how this could be done in Python:

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from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the keywords you want to use to generate the title
keywords = ["quick", "brown", "fox"]
# Tokenize the keywords using a pre-trained language model
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokens = tokenizer.encode(keywords)
# Load a pre-trained language model
language_model = AutoModelForCausalLM.from_pretrained("gpt2")
# Generate candidate titles from the keywords
candidate_titles = []
for i in range(3):
title = language_model.generate(tokens, max_length=8)
title = tokenizer.decode(title[0])
candidate_titles.append(title)
# Select the most catchy and relevant title
selected_title = candidate_titles[0]
for title in candidate_titles:
if is_catchy(title) and is_relevant(title, keywords):
selected_title = title
break
print(selected_title)

This example generates three candidate titles from a set of keywords using a pre-trained language model, and then selects the most catchy and relevant title as the final title. Of course, this is just a simple example to illustrate the basic concept of using a language model to generate a catchy title from keywords. In practice, you would need to use a much larger corpus of text to train the language model and generate more accurate and diverse titles.

setup tty

i don’t think this will work on android, but let’s see?

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ttyd -p <port> -c <username>:<password> <shell_path>
# don't specify interface since that will screw things up

setup x11vnc and novnc

notice novnc has clipboard function now. share clipboard content across devices via the sidebar menu,

in reference of kali official

x11vnc is mirroring the current x11 session. i set it without password.

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#retrieved from fish history
x11vnc -threads -forever

then launch novnc server

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novnc  --vnc localhost:5900 --listen 10020

use this url to access from chromebook:

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http://<kali_ip>:10020/vnc.html?host=<kali_ip>&port=10020

oss-fuzz supports fuzzing C/C++, Rust, Go, Python and Java/JVM code

blog about greyone Discover Vulnerabilities with Flow Sensitive Fuzzing

AFL（American Fuzzy Lop）是由安全研究员Michał Zalewski（@lcamtuf）开发的一款基于覆盖引导（Coverage-guided）的模糊测试工具，它通过记录输入样本的代码覆盖率，从而调整输入样本以提高覆盖率，增加发现漏洞的概率

intro-to-american-fuzzy-lop-fuzzing-in-5-steps

fuzzing-with-american-fuzzy-lop-afl

AFL++ can fuzz c source code, binary targets, network services, gui programs

Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer

seed generation

AI based

Skyfire (learn a probabilistic CFG grammar)

Learn&Fuzz (learn a RNN model of valid inputs)

GAN (learn a GAN to generate legitimate seeds)

Neuzz (learn a NN to model input -> coverage)

Symbolic Execution

Driller

QSYM

DigFuzz

SAVIOR

Intriguer

Matryoshka

HFL

static/dynamic analysis

FANS

seed mutation

AI based

Mopt

LSTM

RL

ILF

program based

VUzzer

GreyOne

efficient testing

coverage metrics