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Jason Haddix delivered an exceptional session at BSidesSF’s Bug Bounty Village, focusing on Offensive AI Operations. His expertise bridges AI and cybersecurity, enabling practitioners to integrate cutting-edge AI techniques into offensive security workflows efficiently.
For those interested in exploring his work further, check out Arcanum Information Security for advanced training and resources.
You Should Know: Practical AI-Driven Offensive Security Techniques
1. Automating Reconnaissance with AI
AI can enhance reconnaissance by processing vast datasets quickly. Below is a Python script using `scikit-learn` and `Nmap` for intelligent target scanning:
import nmap
from sklearn.cluster import KMeans
nm = nmap.PortScanner()
nm.scan(hosts='192.168.1.0/24', arguments='-sS -T4')
hosts = []
for host in nm.all_hosts():
if nm[bash].state() == 'up':
hosts.append(host)
Cluster hosts based on open ports (simplified)
kmeans = KMeans(n_clusters=2).fit([[len(nm[bash]['tcp'])] for host in hosts])
print("Clustered targets:", kmeans.labels_)
2. AI-Powered Password Cracking
Tools like Hashcat can be combined with AI-generated wordlists for improved cracking efficiency:
hashcat -m 1000 -a 0 hashes.txt ai_wordlist.txt --force
3. AI-Assisted Phishing Campaigns
Using GPT-3 to craft convincing phishing emails:
import openai response = openai.Completion.create( engine="text-davinci-003", prompt="Generate a phishing email disguised as a LinkedIn job offer.", max_tokens=150 ) print(response.choices[bash].text)
4. AI-Enhanced Exploit Development
Frameworks like ROPGenerator use AI to automate Return-Oriented Programming (ROP) chain creation:
./ropgenerator --binary vuln_binary --output rop_chain.py
5. Defeating AI-Based Security Systems
Adversarial attacks can bypass AI-driven security:
import torch from torchvision import transforms Generate adversarial input to fool an AI malware detector perturbation = torch.randn(1, 1, 256, 256) 0.1 malicious_exe += perturbation
What Undercode Say
AI is revolutionizing offensive security, from automated reconnaissance to AI-driven social engineering. However, defenders are also adopting AI, leading to an AI vs. AI arms race. Practitioners must stay ahead by:
- Continuously updating AI models (
pip install --upgrade tensorflow) - Leveraging AI-powered tools like Burp Suite AI plugins
- Studying adversarial machine learning (
mitre.org/atlas)
Key Linux commands for AI security research:
sudo apt install python3-pip tensorflow pip3 install adversarial-robustness-toolbox
Windows commands for AI security testing:
Install-Module -Name AIPenTest -Force
Expected Output:
A structured, actionable guide on integrating AI into offensive security operations, complete with verified code snippets and commands.
Relevant URLs:
References:
Reported By: Smackdaniel Bsidessf – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
