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Introduction:
Cyber Threat Intelligence (CTI) has long struggled with the avalanche of CVSS-driven vulnerability data, often prioritizing false positives over real-world exploitability. Enter Mallory.ai – an AI-powered platform from Jonathan Cran (ex-Mandiant) that redefines vulnerability prioritization, exposure investigation, and threat hunting. Unlike traditional SOC workflows that drown analysts in alerts, Mallory’s machine learning models assess actual exploitability, reducing investigation time from days to minutes.
Learning Objectives:
- Understand how AI-driven vulnerability prioritization outperforms CVSS scores using real-world exploit data.
- Execute rapid exposure investigations and automated threat hunting queries with Mallory’s API and CLI tools.
- Integrate AI-powered CTI into existing Linux/Windows security workflows, including SIEM and cloud hardening.
You Should Know:
1. Real‑World Vulnerability Prioritization: Beyond CVSS with Mallory
Traditional vulnerability scanners rely on CVSS scores, which ignore attack context. Mallory’s prioritization engine (https://lnkd.in/grJWFC6c) uses exploitability metrics from live threat feeds, dark web chatter, and weaponized exploit code. Here’s how to replicate the logic in your environment using open-source tools combined with Mallory’s output.
Step‑by‑step guide:
- Extract Mallory’s prioritized vulnerability list – After signing up for the free trial (https://lnkd.in/geUXbPix), navigate to the “Vulnerability Prioritization” dashboard. Export the CSV containing fields:
CVE,ExploitDB_ID,RealWorld_Exploitability_Score,Patch_Availability. - Cross‑check with local assets – Use Nmap and Vulners script to map CVEs to your network:
nmap -sV --script vulners --script-args mincvss=7.0 192.168.1.0/24 -oN vuln_scan.txt
- Filter by Mallory’s exploitability score – Write a Python script to merge both outputs:
import pandas as pd mallory_df = pd.read_csv('mallory_priorities.csv') local_df = pd.read_csv('nmap_vulns.csv') merged = pd.merge(local_df, mallory_df, on='CVE') urgent = merged[merged['RealWorld_Exploitability_Score'] > 8.5] urgent.to_csv('urgent_patches.csv', index=False) - Automate patching – On Linux (Debian/Ubuntu), create a cron job that reads `urgent_patches.csv` and runs `apt-get install –only-upgrade ` for affected packages. On Windows, use PowerShell:
$urgent = Import-Csv urgent_patches.csv $urgent | ForEach-Object { Get-WindowsUpdate -KBArticleID $_.KB -Install }
2. Rapid Exposure Investigation in Minutes
Mallory’s exposure investigation feature (https://lnkd.in/gx7px7Mw) answers “Are we vulnerable?” within minutes. Instead of manual asset inventories, it queries your public‑facing IPs, DNS records, and cloud assets. Here’s how to emulate a lightweight exposure check using Mallory’s API and command‑line tools.
Step‑by‑step guide:
- Generate an API key from Mallory’s settings → API Access.
- Use `curl` to submit your asset list – Create a file `assets.txt` with IPs or domains, then:
curl -X POST https://api.mallory.ai/v1/exposure \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"assets": ["203.0.113.5", "example.com"]}' \ -o exposure_report.json - Parse the JSON with `jq` to extract exposed services and misconfigurations:
jq '.exposed_services[] | {port: .port, service: .name, risk: .risk_level}' exposure_report.json - Automate continuous monitoring – Schedule a Linux systemd timer or Windows Task Scheduler to run the above curl every 6 hours. If a new critical exposure (e.g., port 22 open with weak SSH version) appears, send an alert via Slack webhook.
3. AI‑Generated Threat Hunting Guides for Emerging Threats
Mallory’s threat hunting module (https://lnkd.in/gYk3zhB2) extracts TTPs (Tactics, Techniques, and Procedures) from raw intelligence feeds and generates ready‑to‑use hunt queries. Instead of spending hours writing Sigma rules, you get a step‑by‑step hunt guide.
Step‑by‑step guide:
- Input an emerging threat – e.g., “CVE‑2024‑1234 ransomware campaign” into Mallory’s “Threat Hunting” dashboard.
2. Receive output – Mallory produces:
- MITRE ATT&CK mapping (e.g., T1059 – Command and Scripting Interpreter)
- Sigma rule in YAML format
- KQL (Kusto Query Language) for Microsoft Sentinel
- Splunk SPL for Windows Event Logs
- Deploy the Sigma rule using `sigmac` (Sigma CLI) on Linux:
sigmac -t splunk -c tools/sigma/config/splunk.yml mallory_rule.yml > hunt_search.spl
- Run the query across your SIEM – For Elastic Stack, convert Sigma to EQL:
sigmac -t es-qs mallory_rule.yml > elastic_query.txt curl -X GET "http://localhost:9200/_search?pretty" -H 'Content-Type: application/json' -d "@elastic_query.txt"
- Windows‑native hunting – If using PowerShell transcripts or Sysmon logs, translate Mallory’s TTPs into a script that scans
Get-WinEvent:$events = Get-WinEvent -FilterHashtable @{LogName='Microsoft-Windows-Sysmon/Operational'; ID=1} $suspicious = $events | Where-Object { $<em>.Message -match "powershell -enc" -or $</em>.Message -match "whoami" } $suspicious | Export-Csv -Path "hunt_results.csv" -
Integrating Mallory into Your SOC Workflow with SIEM and SOAR
To maximize efficiency, plug Mallory’s outputs directly into your incident response pipeline. Below are commands to forward prioritized vulnerabilities and hunting guides to open‑source SIEMs like Wazuh or TheHive.
Step‑by‑step guide:
- Set up a webhook listener on your SOAR (e.g., TheHive). Use Python Flask:
from flask import Flask, request app = Flask(<strong>name</strong>) @app.route('/webhook/mallory', methods=['POST']) def handle_mallory(): data = request.json Create alert in TheHive return "OK", 200 - Configure Mallory to send POST requests to your webhook URL (found under Integrations → Custom Webhook).
- For Wazuh (Linux), write a custom decoder that ingests Mallory’s JSON alerts into
/var/ossec/logs/alerts/mallory.json:echo '{"rule_id": "100001", "description": "Critical vulnerability per Mallory"}' >> /var/ossec/logs/alerts/mallory.json
Then restart Wazuh manager: `systemctl restart wazuh-manager`.
- Windows Event Log integration – Use `Write-EventLog` to convert Mallory’s API response into a Windows event:
$alert = (Invoke-RestMethod -Uri "https://api.mallory.ai/v1/alerts" -Headers @{Authorization="Bearer $env:MALLORY_API_KEY"}).alerts[bash] Write-EventLog -LogName "Application" -Source "MalloryCTI" -EventId 5001 -EntryType Warning -Message "Mallory: $($alert.vulnerability) - Exploitability: $($alert.score)"
5. Cloud Hardening Using Mallory’s Exposure Data
Mallory can detect misconfigured cloud assets (e.g., open S3 buckets, overprivileged IAM roles). Here’s a practical cloud hardening pipeline for AWS using Mallory’s findings.
Step‑by‑step guide:
- Run Mallory’s cloud exposure scan – Provide your AWS account ID (read‑only role ARN) via the Mallory dashboard. The platform returns a list of public S3 buckets, unrestricted security groups, and unused access keys.
- Automated remediation with AWS CLI – For each exposed S3 bucket, apply a bucket policy to block public access:
aws s3api put-bucket-acl --bucket exposed-bucket-name --acl private aws s3api put-public-access-block --bucket exposed-bucket-name --public-access-block-configuration "BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true"
- For Azure – Use `az cli` to revoke overly permissive NSG rules:
az network nsg rule delete --nsg-name MalloryIdentifiedNSG --name OpenSSHToInternet --resource-group myRG
- Schedule a daily Lambda function that pulls Mallory’s API, compares with desired cloud state, and auto‑fixes using Python’s
boto3. This creates a self‑healing cloud posture. -
Mitigating Real‑World Exploits: From Mallory Alert to Patch
When Mallory flags a vulnerability with a known exploit (e.g., CVE‑2023‑44487 – HTTP/2 Rapid Reset), follow this mitigation workflow.
Step‑by‑step guide:
- Confirm exploitability – Search Exploit‑DB or use `searchsploit` on Kali Linux:
searchsploit "HTTP/2 Rapid Reset"
- Apply temporary mitigations – For web servers (NGINX/Apache), disable HTTP/2 or rate‑limit:
nginx.conf http { limit_req_zone $binary_remote_addr zone=one:10m rate=10r/s; server { location / { limit_req zone=one burst=20; } } }
3. Permanent patching – On Ubuntu:
sudo apt update && sudo apt install --only-upgrade nginx sudo systemctl restart nginx
On Windows Server with IIS, use the cumulative update from Microsoft Update Catalog:
wget https://catalog.update.microsoft.com/Download.aspx -OutFile http2-patch.msu wusa http2-patch.msu /quiet /norestart
7. Community Edition Expectations – Open Source Alternatives
Word has it that Mallory may release a community edition. While waiting, you can build a simplified AI‑vulnerability prioritizer using open‑source LLMs (e.g., Ollama + Mistral) and the National Vulnerability Database (NVD) feed.
Step‑by‑step guide:
- Download NVD CVE feed – Use `curl` to get the latest JSON:
curl -O https://nvd.nist.gov/feeds/json/cve/2.0/nvdcve-2.0-2025.json.zip unzip nvdcve-2.0-2025.json.zip
- Extract real‑world exploitability indicators – Filter CVEs with references to Exploit‑DB, Metasploit, or GitHub PoCs using
jq:jq '.vulnerabilities[] | select(.cve.references[] | .url | contains("exploit-db") or contains("metasploit"))' nvdcve-2.0-2025.json - Use a local LLM (Ollama) to summarize – Pipe the output to Mistral for a human‑readable prioritization:
echo "List the top 5 most critical CVEs with active exploits" | ollama run mistral
- Schedule this as a daily cron job and email the report to your team. It’s not Mallory’s AI, but it’s a free starting point.
What Undercode Say:
- Key Takeaway 1: Mallory shifts vulnerability management from CVSS theory to real‑world exploitability, directly reducing false positives and patching fatigue.
- Key Takeaway 2: The platform’s threat hunting automation turns raw TTPs into executable Sigma/KQL queries, slashing investigation time from hours to minutes.
Analysis: Traditional CTI workflows are reactive and manual. By embedding AI into prioritization, exposure discovery, and hunt guide generation, Mallory addresses the core problem of alert overload. Its API‑first design allows seamless integration with existing SIEM/SOAR, while the upcoming community edition could democratize AI‑powered CTI for smaller teams. However, organizations must still verify AI‑generated recommendations against their unique environment – blind trust in any tool remains a risk. The provided Linux/Windows commands demonstrate how to operationalize these insights, turning Mallory from a dashboard into an active defender.
Prediction:
Within 18 months, AI‑driven vulnerability intelligence platforms like Mallory will become standard in mid‑to‑large enterprises, pushing legacy CVSS‑only scanners to extinction. The community edition, if released, will catalyze adoption among startups and educational institutions, leading to a surge in automated threat hunting and real‑time exposure monitoring. Expect also a rise in adversarial AI – attackers will attempt to poison vulnerability feeds to manipulate Mallory’s prioritization. Defenders must therefore maintain hybrid human‑AI review loops, ensuring that the speed of AI does not outpace the accuracy of human judgment.
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