🦑ARP and DNS Spoofing:


> Network Penetration Testing: Assess the security of networks by identifying weaknesses in ARP protocols and DNS resolutions. 🔍🛡

>Security Auditing: Log and analyze network traffic to discover potential vulnerabilities and improve network defenses. 📊🔒

> Educational Purposes: Learn and teach network security concepts through practical, hands-on experience with ARP and DNS spoofing techniques. 🎓📚

>Traffic Analysis: Monitor and capture traffic for forensic investigations or to understand user behavior on a network. 🔍📈


Installation 🛠
To install and run BlackVenom, follow these simple steps:

1️⃣ Create a Python Virtual Environment 🐍
First, create a virtual environment to manage dependencies:

python -m venv BlackVenom-Kali

2️⃣ Activate the Virtual Environment 🔑
Activate the virtual environment:

source BlackVenom-Kali/bin/activate

3️⃣ Install Dependencies 📦
Now, install the necessary dependencies from the requirements.txt file:

pip install -r requirements.txt

> Run the Tool ⚡️ After installation, you can run BlackVenom using the provided CLI:
python black_venom_cli.py
Usage Examples
Example 1: Basic ARP Spoofing
This command performs a basic ARP spoofing attack between a target and a gateway without enabling packet logging or DNS spoofing. 🔗

sudo python black_venom_cli.py \
    --target_ip 192.168.11.128 \
    --gateway_ip 192.168.11.2 \
    --interface eth0

Example 2: ARP Spoofing with Traffic Logging
In this example, packet logging is enabled while performing ARP spoofing. 📝

sudo python black_venom_cli.py \
    --target_ip 192.168.11.128 \
    --gateway_ip 192.168.11.2 \
    --interface eth0 \
    --enable_logging \
    --log_file ~/Desktop/captured_packets.pcap
Example 3: ARP Spoofing and DNS Spoofing
This command enables both ARP spoofing and DNS spoofing, redirecting DNS requests for a specific domain. 🌐🔀

sudo python black_venom_cli.py \
    --target_ip 192.168.11.128 \
    --gateway_ip 192.168.11.2 \
    --interface eth0 \
    --enable_logging \
    --log_file ~/Desktop/captured_packets.pcap

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

🔐 Hackers Leak Private Photos of WWE Star Paige

https://undercodenews.com/hackers-leak-private-photos-of-wwe-star-paige/

@Undercode_News

🔍 Pakistan's #Digital Pulse: Top #Google Searches of 2024

https://undercodenews.com/pakistans-digital-pulse-top-google-searches-of-2024/

@Undercode_News

🦑Spoofing Utilities:

DNS-Spoof by Mustafa Dalga:
https://github.com/mustafadalga/dns-spoof

MITM Attack Practice:
https://github.com/bilalz5-github/MITM-Attack_practice

ARP Spoof Detection Tool (Dr. Spoof):
https://github.com/Enixes/Dr.Spoof

AdBleed (DNS Redirection Tool):
https://github.com/arevaclier/AdBleed

DNS Packet Injection:
https://github.com/shreyasbhatia09/DNS-Packet-Injection

PyDNS (Python DNS Server):
https://github.com/Douile/pydns

Rock-DDOS (Includes ARP Spoofing):
https://github.com/MasonDye/Rock-DDOS

NetSpionage:
https://github.com/ANG13T/netspionage

Dead Drop (Network Steganography with Spoofing):
https://github.com/kerosene5/Dead_Drop

ATA-Shell (ARP Modular Shell):
https://github.com/shelbenheimer/ata-shell

Phishing with DNS Spoofing Demo:
https://github.com/chi-0828/Phishing-with-DNS-spoofing

RITM (Roast in the Middle for MITM):
https://github.com/Tw1sm/RITM

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

Blackview Hero 10: A Flawed Foldable Attempt

https://undercodenews.com/blackview-hero-10-a-flawed-foldable-attempt/

@Undercode_News

Don't Fall for the Hype! NITDA Warns of Deceptive Festive Season Scams

https://undercodenews.com/dont-fall-for-the-hype-nitda-warns-of-deceptive-festive-season-scams/

@Undercode_News

Israel's #AI Talent Boost: A Strategic Move

https://undercodenews.com/israels-ai-talent-boost-a-strategic-move/

@Undercode_News

🛡️ Stigg Secures 75M to Revolutionize #AI and SaaS Pricing

https://undercodenews.com/stigg-secures-75m-to-revolutionize-ai-and-saas-pricing/

@Undercode_News

Beware of One-Ring Scams: Don't Fall for the Wangiri Trick

https://undercodenews.com/beware-of-one-ring-scams-dont-fall-for-the-wangiri-trick/

@Undercode_News

🛡️ #TikTok's Future in the US: A Battle for Data and National Security

https://undercodenews.com/tiktoks-future-in-the-us-a-battle-for-data-and-national-security/

@Undercode_News

⚡️ #Microsoft's #Windows 11: A Mixed Bag of Features and Frustrations

https://undercodenews.com/microsofts-windows-11-a-mixed-bag-of-features-and-frustrations/

@Undercode_News

🔋 PoS Fees on the Rise: How Fintech Charges Impact Cash Transactions

https://undercodenews.com/pos-fees-on-the-rise-how-fintech-charges-impact-cash-transactions/

@Undercode_News

🚨 A Stealthy Threat: Winnti's New PHP Backdoor, Glutton

https://undercodenews.com/a-stealthy-threat-winntis-new-php-backdoor-glutton/

@Undercode_News

🚨 A Stealthy Threat: The Rise of PUMAKIT, a Sophisticated #Linux Rootkit

https://undercodenews.com/a-stealthy-threat-the-rise-of-pumakit-a-sophisticated-linux-rootkit/

@Undercode_News

🎮 RTX 4070 #Gaming #Laptop for 99: A Steal Deal

https://undercodenews.com/rtx-4070-gaming-laptop-for-99-a-steal-deal/

@Undercode_News

🎮 #Gaming Laptops: A Gamer's Dream Machine

https://undercodenews.com/gaming-laptops-a-gamers-dream-machine/

@Undercode_News

🦑raditional Blue Team Techniques on Steroid with LLM Honeypots 🛡

Honeypots are not new. Still, you can re-innovate how it works with the technology - this time with LLM. Honeypots can be a critical tool for detecting and analyzing malicious activity. But what if we could take them to the next level? Enter LLM Honeypots—a groundbreaking approach leveraging the power of LLMs to create advanced, interactive traps for attackers.

🔍 What sets LLM Honeypots apart?

Traditional honeypots often rely on static or semi-dynamic environments. In contrast, LLMs introduce context-aware, adaptive interactions, enabling a honeypot to mimic real systems and user behaviors more convincingly. Imagine an attacker interacting with a "system" that not only responds but learns and adapts in real time.

💡 Key Innovations:

1️⃣ Dynamic Interaction: LLMs can simulate realistic system responses, mimicking human-like behavior.
2️⃣ Data Harvesting: They help collect rich telemetry, offering insights into attacker methodologies.
3️⃣ Deception at Scale: LLMs enhance deception, making it harder for adversaries to distinguish honeypots from legitimate systems.

🔐 Why It Matters: This approach can provide security teams with a treasure trove of intelligence, from understanding new attack vectors to proactively defending against them. It’s a leap forward in using AI to protect and outsmart attackers.

🧠 Future Implications: Integrating LLMs into honeypot systems could redefine cybersecurity strategies as AI evolves. From training SOC teams to crafting defense mechanisms, the possibilities are endless.

The use of LLM Honeypots to interact with attackers and gather insights. Here's a potential flow:
1️⃣ Attacker Interaction: The attacker interacts with the system, believing it legit.
2️⃣ Honeypot Interaction: The interaction is routed to a honeypot, a system designed to mimic real environments while capturing malicious behaviors.
3️⃣ Data Collection & Analysis: The honeypot collects telemetry, including input patterns and attacker strategies. Then, the data is processed and analyzed.
4️⃣ Model Integration: The analyzed data is leveraged to enhance machine learning models or decision systems, potentially an LLM.
5️⃣ Feedback: The refined model can improve its security posture & response.

Ref: Elli Shlomo
@UndercodeCommunity
▁ ▂ ▄ U𝕟𝔻Ⓔ𝐫Ć𝔬𝓓ⓔ ▄ ▂ ▁

🚨 Struts2 Under Attack: New Vulnerability Exploited in the Wild

https://undercodenews.com/struts2-under-attack-new-vulnerability-exploited-in-the-wild/

@Undercode_News