Listen to this Post
Introduction:
Large language models are no longer just chatbots—they’ve evolved into autonomous vulnerability hunters. Anthropic’s new Mythos model has reportedly discovered thousands of high-severity zero-day vulnerabilities across major operating systems, browsers, and enterprise software, demonstrating capabilities that surpass top human security experts. In response, Project Glasswing deploys this AI to proactively secure critical systems before malicious actors can weaponize the same flaws.
Learning Objectives:
- Understand how LLMs like Mythos automate zero-day discovery at scale
- Implement AI-assisted vulnerability scanning and system hardening workflows
- Master incident response and patch management for AI-discovered threats
You Should Know:
- Leveraging LLMs for Zero-Day Discovery: A Technical Deep Dive
Mythos uses a combination of static code analysis, fuzzing, and semantic reasoning to identify memory corruption, injection flaws, and logic bugs. While the exact model weights are proprietary, you can replicate the pipeline using open-source tools and AI APIs.
Step‑by‑step guide to emulate AI‑driven scanning:
- Set up a vulnerability scanner with LLM integration
Use `nmap` to discover open services, then feed results into an LLM for analysis.Linux: Scan a target network nmap -sV -sC -oA target_scan 192.168.1.0/24 Extract service versions grep -E "open|version" target_scan.nmap > services.txt
- Use Python to call an LLM (e.g., API) for zero‑day prediction
import requests headers = {"x-api-key": "YOUR_API_KEY", "anthropic-version": "2023-06-01"} data = { "model": "-mythos-20250101", "max_tokens": 1000, "messages": [{"role": "user", "content": "Analyze this service banner for potential zero-days: " + open("services.txt").read()}] } response = requests.post("https://api.anthropic.com/v1/messages", headers=headers, json=data) print(response.json()["content"])
3. Automate fuzzing with AI‑generated inputs
Install AFL++ and radamsa for mutation fuzzing sudo apt install afl++ radamsa Generate seed corpus echo "GET / HTTP/1.1" > seed.txt Run fuzzer on a target binary afl-fuzz -i seed/ -o findings/ ./target_binary @@
What this does: The combination of AI reasoning and traditional fuzzing uncovers edge cases that human analysts often miss. Use it in CI/CD pipelines to catch zero‑days before release.
2. Project Glasswing Deployment Guide: Securing Critical Infrastructure
Project Glasswing is a real‑time defensive framework that ingests AI‑discovered vulnerability reports and automatically deploys mitigations. You can build a similar system using open‑source SIEM and orchestration tools.
Step‑by‑step to deploy a Glasswing‑like architecture:
- Install Wazuh (SIEM + XDR) on a Ubuntu server
curl -sO https://packages.wazuh.com/4.x/wazuh-install.sh sudo bash wazuh-install.sh -a Access web UI at https://<your-ip>
- Create a custom rule to alert on AI‑reported CVEs
<!-- /var/ossec/etc/rules/local_rules.xml --> <rule id="100010" level="15"> <if_sid>1000</if_sid> <match>CLAUDE_MYTHOS_ZERO_DAY</match> <description>Zero-day detected by AI – immediate response required</description> </rule>
3. Automate firewall blocking via active response
Add to /var/ossec/etc/ossec.conf <active-response> <command>firewall-drop</command> <location>local</location> <rules_id>100010</rules_id> </active-response>
4. Windows equivalent: Use PowerShell to block malicious IPs
Run as Admin New-NetFirewallRule -DisplayName "AI_ZeroDay_Block" -Direction Inbound -RemoteAddress 203.0.113.0 -Action Block
How to use: Integrate an LLM webhook that pushes zero‑day indicators to Wazuh’s API. The system then quarantines affected endpoints within seconds.
3. Automated Vulnerability Patching with AI Integration
AI models can recommend precise patches and even generate hotfix code. Here’s how to operationalise those recommendations.
Step‑by‑step automated patching:
1. Set up Ansible for multi‑OS patch management
playbook.yml - hosts: all tasks: - name: Update apt cache (Ubuntu/Debian) apt: update_cache: yes when: ansible_os_family == "Debian" - name: Install critical security updates win_updates: category_names: SecurityUpdates state: installed when: ansible_os_family == "Windows"
2. Run the playbook from a controller
ansible-playbook -i inventory.ini playbook.yml --extra-vars "ai_recommended_cve=CVE-2025-1234"
3. For kernel zero‑days, use live patching (Linux)
Install kpatch (Red Hat) or canonical-livepatch (Ubuntu) sudo canonical-livepatch enable YOUR_TOKEN Apply specific patch sudo canonical-livepatch patch --cve CVE-2025-1234
What this does: Reduces mean‑time‑to‑patch from weeks to minutes, especially critical when AI discloses a zero‑day before a vendor patch exists.
4. OS and Browser Hardening Against AI‑Discovered Flaws
Since Mythos finds flaws across major OSes and browsers, proactive hardening is essential.
Step‑by‑step for Linux (Ubuntu 22.04+):
1. Enable AppArmor and enforce profiles
sudo aa-enforce /etc/apparmor.d/usr.bin.firefox sudo systemctl restart apparmor
2. Set kernel parameters to mitigate memory corruption
echo "kernel.randomize_va_space=2" | sudo tee -a /etc/sysctl.conf echo "kernel.kptr_restrict=2" | sudo tee -a /etc/sysctl.conf sudo sysctl -p
Step‑by‑step for Windows 11:
1. Enable Windows Defender Application Control (WDAC)
Generate a base policy in audit mode New-CIPolicy -Level Publisher -FilePath C:\WDAC\BasePolicy.xml -UserPEs Convert to binary and deploy ConvertFrom-CIPolicy -XmlFilePath C:\WDAC\BasePolicy.xml -BinaryFilePath C:\WDAC\SiPolicy.p7b Copy to EFI partition and reboot Copy-Item C:\WDAC\SiPolicy.p7b C:\EFI\Microsoft\Boot\SiPolicy.p7b
2. Configure Attack Surface Reduction (ASR) rules
Set-MpPreference -AttackSurfaceReductionRules_Ids 75668C1F-73B5-4CF0-BB93-3ECF5CB7CC84 -AttackSurfaceReductionRules_Actions Enabled
Why this matters: Hardening blocks common exploitation vectors even if the zero‑day itself isn’t patched.
5. Training Courses for Next‑Gen Cyber AI Defense
To stay ahead of AI‑driven threats, professionals need hands‑on courses. Recommended training includes:
– SANS SEC699: Purple Team Tactics – AI & Automation (focus on using LLMs for detection)
– Coursera – “AI for Cybersecurity” by DeepLearning.AI (builds basic neural network threat classifiers)
– OffSec’s OSWA (Offensive Security Web Assessor) with AI‑augmented fuzzing modules
Lab setup for self‑study:
Clone a vulnerable practice environment git clone https://github.com/vulhub/vulhub.git cd vulhub/nginx/php_8.1_backdoor docker-compose up -d Now feed the target into an LLM to find zero‑days
Windows training VM: Use Windows Sandbox to safely test exploits
Enable Sandbox Enable-WindowsOptionalFeature -FeatureName "Containers-DisposableClientVM" -Online
- API Security in the Age of AI‑Discovered Flaws
APIs are prime targets for zero‑days. Use AI to generate malicious payloads and harden accordingly.
Step‑by‑step API fuzzing with AI:
1. Run OWASP ZAP in headless mode
Linux zap-cli quick-scan --self-contained --spider -r -l Medium http://testapi.example.com/v1
2. Use GraphQL introspection to map attack surface
Install graphql-cop npm install -g graphql-cop graphql-cop http://testapi.example.com/graphql -o report.json
3. Apply AI‑recommended mitigations:
- Rate limiting (Linux with nginx)
limit_req_zone $binary_remote_addr zone=mylimit:10m rate=10r/s;
- Input validation regex generated by :
^[a-zA-Z0-9_-]{3,64}$ AI-suggested pattern for usernames
7. Incident Response for AI‑Detected Zero‑Days
When AI flags a zero‑day, follow this IR playbook.
Step‑by‑step initial triage:
1. Collect forensic evidence (Linux)
sudo mkdir /ir_evidence sudo ps auxwf > /ir_evidence/running_processes.txt sudo netstat -tulpn > /ir_evidence/listening_ports.txt sudo cp /var/log/syslog /ir_evidence/
2. Windows equivalent with PowerShell
Get-Process | Export-Csv C:\ir_evidence\processes.csv Get-NetTCPConnection | Export-Csv C:\ir_evidence\connections.csv Get-WinEvent -LogName Security -MaxEvents 500 | Export-Csv C:\ir_evidence\security_events.csv
3. Isolate the affected host
Linux: Drop all traffic except to IR server sudo iptables -P INPUT DROP sudo iptables -P OUTPUT ACCEPT only to IR collector
Windows: Disable all network interfaces Get-NetAdapter | Disable-NetAdapter -Confirm:$false
4. Query AI for root cause analysis – Send the collected logs to Mythos via its API and ask for a summary of the exploitation method and containment steps.
What Undercode Say:
- AI is now a double‑edged sword: The same model that finds zero‑days for defense can be stolen or misused by attackers. Organisations must treat LLM weights as critical intellectual property and implement model‑hardening measures.
- Proactive defense is finally feasible: Project Glasswing demonstrates that real‑time, AI‑driven patching and micro‑segmentation can shrink the window of exposure from months to milliseconds. However, false positives remain a challenge—human‑in‑the‑loop validation is still required for high‑impact systems.
- Upskilling is non‑negotiable: The 57 certifications held by Tony Moukbel (the LinkedIn user sharing this news) highlight the depth needed to operate at this intersection. Security teams must add LLM prompt engineering, API security, and automation scripting to their core competencies.
Prediction:
By 2027, every major SOC will integrate an AI vulnerability discovery model as a standard tool—just like Nmap or Burp Suite today. This will trigger a “zero‑day arms race” where attackers use adversarial LLMs to evade detection while defenders use models like Mythos. Regulation will likely require disclosure of AI‑discovered zero‑days to a central clearinghouse within 24 hours. Organisations that fail to adopt AI‑driven proactive hardening will experience breach costs 5–10× higher than those that do. The era of waiting for vendor patches is ending; real‑time AI defence is the new baseline.
▶️ Related Video (84% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Hackermohitkumar Anthropics – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
