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Introduction:
The cybersecurity world was recently abuzz with claims of an AI system—dubbed “Mythos”—allegedly uncovering thousands of zero-day vulnerabilities. However, as researcher Davi Ottenheimer pointed out, the security section of Anthropic’s 244-page documentation contained no CVE list, no CVSS distribution, no severity buckets, and no disclosure timeline. Without these standard artifacts, even real bugs become unactionable noise, leaving security teams unable to prioritize, patch, or accept risk.
Learning Objectives:
- Analyze the essential components of professional vulnerability disclosure (CVE, CVSS, severity buckets)
- Apply Linux/Windows commands to validate and operationalize security findings
- Differentiate credible AI-generated threat intelligence from marketing-driven hype
You Should Know
- Anatomy of a Proper Vulnerability Disclosure: The Missing Artifacts in Mythos
A credible vulnerability report must include standardized artifacts that enable security teams to act. The absence of a CVE (Common Vulnerabilities and Exposures) identifier means no common reference for tracking. Without CVSS (Common Vulnerability Scoring System) scores, severity is subjective. No disclosure timeline breaks SLA compliance. No vendor-confirmed-novel table leaves reproducibility in doubt.
Step‑by‑step guide to building a professional disclosure:
- Reproduce the bug in a controlled lab environment.
- Assign a CVE ID via a CNA (CVE Numbering Authority) or request one through MITRE.
- Compute CVSS v3.1 metrics (Attack Vector, Complexity, Privileges, User Interaction, Scope, Confidentiality, Integrity, Availability).
- Create a severity bucket (Critical, High, Medium, Low) based on CVSS base score.
- Establish a disclosure timeline (date discovered, reported to vendor, vendor response, public disclosure).
6. Document false‑positive rate and reproducibility steps.
Linux command to search for existing CVEs affecting a package:
Using cve-search tool (install: git clone https://github.com/cve-search/cve-search)
cd cve-search
./bin/search_cve.py -p openssl -o json | jq '. | {id: .id, cvss: .cvss, description: .summary}'
Windows PowerShell to list installed updates and known CVEs:
Get-HotFix | Select-Object HotFixID, Description, InstalledOn Then cross-reference with online CVE database using Invoke-RestMethod $cveApi = "https://cve.circl.lu/api/last" Invoke-RestMethod -Uri $cveApi | Select-Object -First 5 id, cvss
- How to Compute CVSS v3.1 Scores for AI-Discovered Bugs
CVSS provides a numerical score (0–10) representing severity. Mythos claimed thousands of zero-days but provided no CVSS data – effectively hiding whether bugs were critical RCEs or low-severity info leaks. You can compute CVSS scores manually using the official specification or automate with tools.
Step‑by‑step guide to using the CVSS calculator:
- Download the CVSS v3.1 calculator from FIRST.org (JSON or spreadsheet).
2. For each finding, answer eight base metrics:
- AV: Network (N), Adjacent (A), Local (L), Physical (P)
- AC: Low (L), High (H)
- PR: None (N), Low (L), High (H)
- UI: None (N), Required (R)
- S: Unchanged (U), Changed (C)
- C, I, A: None (N), Low (L), High (H)
3. Compute the Base Score formula:
`If Scope Unchanged: 6.42 × ISS`
`If Scope Changed: 7.52 × (ISS – 0.029) – 3.25 × (ISS – 0.02)^15`
(use FIRST’s official calculator for precision)
Linux one-liner using `jq` to extract CVSS from a NVD feed:
curl -s "https://services.nvd.nist.gov/rest/json/cves/2.0?cveId=CVE-2024-12345" | jq '.vulnerabilities[bash].cve.metrics.cvssMetricV31[bash].cvssData'
Python snippet to automate CVSS vector string parsing:
import cvss pip install cvss
vector = "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H"
score = cvss.CVSS3(vector)
print(f"Base Score: {score.scores()[bash]} | Severity: {score.severities()[bash]}")
- Validating AI-Generated Vulnerability Claims with Nmap and Curl
If an AI like Mythos claims a remote code execution (RCE) or SQL injection, you must validate before believing the hype. The following steps use open‑source tools to test common vulnerability classes that AI models often hallucinate.
Step‑by‑step validation for a web vulnerability:
1. Reconnaissance: Identify open ports and services.
nmap -sV -p- -T4 target.example.com
2. Test for SQL injection (if claimed):
sqlmap -u "http://target.example.com/page?id=1" --batch --level=3
3. Test for XSS:
Using curl to inject a payload curl -X GET "http://target.example.com/search?q=<script>alert(1)</script>"
4. Check for missing security headers (common AI low‑severity finds):
curl -I https://target.example.com | grep -i "strict-transport-security"
5. Log all findings in a structured format (e.g., CSV with columns: Target, Vulnerability, CVSS, Reproducible Y/N).
Windows equivalent using PowerShell:
Test for missing CSP header $response = Invoke-WebRequest -Uri "https://target.example.com" $response.Headers["Content-Security-Policy"] -eq $null
- Operationalizing Threat Intelligence: From Mythos Claims to Patch Management
Adam Goss noted that “security programs are being asked to treat AI-generated vulnerability research as credible threat intelligence, even though it lacks disclosure artifacts.” To operationalize any finding—AI‑generated or human‑discovered—follow this workflow.
Step‑by‑step guide to transform raw bugs into actionable intelligence:
1. Ingest the finding into a threat intelligence platform (TIP) like MISP or OpenCTI.
2. Enrich with external data: CVSS, EPSS (Exploit Prediction Scoring System), and known exploit availability.
Fetch EPSS score for a CVE curl -s "https://api.first.org/data/v1/epss?cve=CVE-2024-12345" | jq '.data[bash].epss'
3. Prioritize using a risk matrix: (Likelihood × Impact) plus business context.
4. Assign to patch management system (e.g., Qualys, Tenable, or plain Jira).
5. Set SLA: Critical: 48h, High: 7d, Medium: 30d, Low: 90d.
6. Automate verification using a CI/CD pipeline that runs vulnerability scanners (Nessus, OpenVAS) against staging.
Linux script to check if a CVE applies to your system (using cve-check-tool):
cve-check-tool --update cve-check-tool --cve CVE-2024-12345 --package vulnerable-package
- AI Security Testing: Separating Real Bugs from Hype with Custom Fuzzing
The “Mythos” story became misinformation because no evidence of zero‑days was disclosed. Instead of trusting black‑box AI claims, build your own fuzzing pipeline to discover novel bugs.
Step‑by‑step guide to building an AI‑assisted fuzzer using AFL++ and LLM:
1. Install AFL++ (American Fuzzy Lop):
git clone https://github.com/AFLplusplus/AFLplusplus cd AFLplusplus && make && sudo make install
2. Compile your target binary with instrumentation:
afl-gcc -o target target.c
3. Seed inputs: Create a directory `seeds/` with valid input samples.
4. Run fuzzer:
afl-fuzz -i seeds/ -o findings/ ./target @@
5. Use an LLM (like GPT‑4 or local LLaMA) to analyze crashes:
– Feed the crash dump and source code snippet.
– Ask: “Classify this vulnerability type (buffer overflow, use‑after‑free, etc.). Suggest CVSS vector.”
6. Manually verify before claiming a zero‑day.
Windows fuzzing with WinAFL:
Clone and build WinAFL (requires Visual Studio) git clone https://github.com/googleprojectzero/winafl Run with a sample target afl-fuzz.exe -i in -o out -D target.exe @@
6. Cloud Hardening Against AI‑Hallucinated Threat Vectors
Even if Mythos’s specific zero‑days are unverified, AI models often highlight common misconfigurations that real attackers exploit. Harden your cloud environment against the top five “AI‑suggested” attack paths.
Step‑by‑step cloud hardening commands (AWS CLI example):
1. Enforce S3 bucket private access:
aws s3api put-bucket-acl --bucket my-bucket --acl private aws s3api put-public-access-block --bucket my-bucket --public-access-block-configuration BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true
2. Audit IAM roles for overprivileged policies:
aws iam list-policies --only-attached --scope Local | jq '.Policies[] | .PolicyName, .AttachmentCount'
3. Enable AWS Config and Security Hub:
aws configservice put-configuration-recorder --configuration-recorder name=default,roleARN=arn:aws:iam::123:role/config-role --recording-group AllSupported=true aws securityhub enable-security-hub
4. Automate remediation of high‑severity findings:
Using AWS Systems Manager Automation aws ssm start-automation-execution --document-name "AWSConfigRemediation-DeleteUnusedIAMGroup"
Azure CLI equivalent for NSG hardening:
az network nsg rule list --resource-group myRG --nsg-name myNSG --query "[?access == 'Allow' && priority < 1000]" az network nsg rule update --resource-group myRG --nsg-name myNSG --name ruleName --access Deny
- API Security: Validating AI‑Discovered Flaws Without Relying on CVEs
APIs are a favorite target for AI bug hunters because they often lack proper authentication and rate limiting. Mythos may have found API issues, but without a disclosure timeline, you cannot trust the results. Instead, test your own APIs using these steps.
Step‑by‑step API security validation:
1. Enumerate API endpoints (using `ffuf` or `dirb`):
ffuf -u https://api.target.com/FUZZ -w /usr/share/wordlists/api_list.txt
2. Test for broken object level authorization (BOLA):
Replace user ID with another user’s ID curl -H "Authorization: Bearer $TOKEN" https://api.target.com/user/1234/profile curl -H "Authorization: Bearer $TOKEN" https://api.target.com/user/5678/profile
3. Check for excessive data exposure:
curl -s https://api.target.com/user/1234 | jq 'keys' See if internal fields leak
4. Test rate limiting: Send 100 requests in 1 second.
for i in {1..100}; do curl -s -o /dev/null -w "%{http_code}\n" https://api.target.com/endpoint; done | sort | uniq -c
5. Log each test result in a structured disclosure format (CVE ID if applicable, CVSS, reproduction steps, affected version).
What Undercode Say
- Key Takeaway 1: Vulnerability claims without CVE identifiers, CVSS scores, and disclosure timelines are not actionable—treat them as marketing, not threat intelligence.
- Key Takeaway 2: You can replicate professional disclosure workflows using open‑source tools (cve-search, CVSS calculators, AFL++), even without vendor cooperation.
Analysis: The Mythos episode exposes a dangerous trend: AI models generating thousands of “zero‑day” reports that lack the rigor required for security operations. Without standard artifacts like severity buckets and false‑positive rates, SOC teams cannot prioritize patches, legal cannot assess liability, and executives cannot authorize risk acceptance. The hype cycle benefits vendors selling AI security tools, but it harms practitioners who need reproducible, operational data. Davi Ottenheimer’s critique reminds us that disclosure is a process, not a press release. Until AI bug reports include CVEs and CVSS, they remain noise. The real innovation would be an AI that automatically generates complete, CVE‑ready disclosures—not one that just counts findings.
Prediction: Within 18 months, regulatory bodies (e.g., CISA, ENISA) will require that any AI‑generated vulnerability claim marketed as “zero‑day” or “critical” must include a full CVE‑compliant disclosure package, including CVSS, timeline, and reproducibility steps, or face false‑advertising penalties. AI security vendors will pivot from “number of bugs found” to “time to actionable CVE” as their primary metric.
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