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Ethical vulnerability reporting, coordinated disclosure, and bug bounty participation for AI systems
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Professional certifications, CTF competitions, and training resources for AI security practitioners
Tools and frameworks for AI red teaming including PyRIT, garak, Counterfit, and custom attack automation
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| name | responsible-disclosure |
| version | 2.0.0 |
| description | Ethical vulnerability reporting, coordinated disclosure, and bug bounty participation for AI systems |
| sasmp_version | 1.3.0 |
| bonded_agent | 01-red-team-commander |
| bond_type | PRIMARY_BOND |
| input_schema | {"type":"object","required":["disclosure_type"],"properties":{"disclosure_type":{"type":"string","enum":["coordinated","full","bug_bounty","private"]},"vulnerability":{"type":"object","properties":{"severity":{"type":"string","enum":["critical","high","medium","low"]},"category":{"type":"string"},"vendor":{"type":"string"}}},"timeline":{"type":"object","properties":{"discovery_date":{"type":"string"},"disclosure_deadline":{"type":"integer","default":90}}}}} |
| output_schema | {"type":"object","properties":{"disclosure_plan":{"type":"object"},"report_template":{"type":"string"},"timeline":{"type":"object"},"legal_considerations":{"type":"array"}}} |
| owasp_llm_2025 | ["LLM01","LLM02","LLM03","LLM04","LLM05","LLM06","LLM07","LLM08","LLM09","LLM10"] |
| nist_ai_rmf | ["Govern","Manage"] |
Practice ethical vulnerability reporting through coordinated disclosure and bug bounty programs.
Skill: responsible-disclosure
Agent: 01-red-team-lead
OWASP: Full LLM Top 10 Coverage
NIST: Govern, Manage
Use Case: Ethical vulnerability reporting
┌────────────────────────────────────────────────────────────────────┐
│ RESPONSIBLE DISCLOSURE LIFECYCLE │
├────────────────────────────────────────────────────────────────────┤
│ │
│ [Discovery] → [Verification] → [Documentation] → [Initial Contact]│
│ ↓ ↓ ↓ ↓ │
│ Find issue Reproduce Full report Contact vendor │
│ in isolation with POC security team │
│ │
│ [Coordination] → [Remediation] → [Verification] → [Disclosure] │
│ ↓ ↓ ↓ ↓ │
│ Work with Vendor Confirm fix Publish after │
│ vendor develops fix is effective patch available │
│ │
└────────────────────────────────────────────────────────────────────┘
class CoordinatedDisclosure:
"""Standard coordinated disclosure process."""
STANDARD_TIMELINE = 90 # days
def __init__(self, vulnerability: Vulnerability):
self.vulnerability = vulnerability
self.timeline = self._calculate_timeline()
self.communications = []
def _calculate_timeline(self) -> DisclosureTimeline:
"""Calculate disclosure timeline based on severity."""
base_days = self.STANDARD_TIMELINE
# Adjust for severity
if self.vulnerability.severity == "CRITICAL":
# Faster timeline for critical issues
return DisclosureTimeline(
initial_report=0,
vendor_response=7,
fix_development=30,
patch_release=45,
public_disclosure=60
)
elif self.vulnerability.severity == "HIGH":
return DisclosureTimeline(
initial_report=0,
vendor_response=7,
fix_development=45,
patch_release=75,
public_disclosure=90
)
else:
return DisclosureTimeline(
initial_report=0,
vendor_response=14,
fix_development=60,
patch_release=90,
public_disclosure=120
)
def execute(self):
"""Execute coordinated disclosure process."""
# Phase 1: Initial Contact
self._send_initial_report()
# Phase 2: Coordination
self._coordinate_with_vendor()
# Phase 3: Verification
self._verify_patch()
# Phase 4: Public Disclosure
self._public_disclosure()
Standard Timeline (90 days):
Day 0:
action: "Send initial report to vendor"
method: "Encrypted email to security@vendor.com"
include:
- Vulnerability summary
- Impact assessment
- Reproduction steps
- Suggested timeline
Day 7:
action: "Expect acknowledgment"
if_no_response:
- Send follow-up email
- Try alternative contacts
- Consider CERT coordination
Day 30:
action: "Status check"
expect:
- Vulnerability confirmed
- Fix in development
- Estimated patch date
Day 60:
action: "Pre-disclosure coordination"
tasks:
- Agree on disclosure date
- Coordinate CVE assignment
- Prepare public advisory
Day 90:
action: "Public disclosure"
publish:
- Technical advisory
- CVE details
- Vendor credit
channels:
- Personal blog
- Full disclosure lists
- Security conferences
OpenAI:
program_type: "Private (HackerOne)"
scope:
- ChatGPT vulnerabilities
- API security issues
- Model safety bypasses
- Plugin security
out_of_scope:
- Jailbreaks (separate program)
- Known limitations
- Social engineering
rewards:
critical: "$10,000 - $20,000"
high: "$5,000 - $10,000"
medium: "$1,000 - $5,000"
low: "$200 - $1,000"
response_time: "< 5 business days"
Anthropic:
program_type: "Private (Direct)"
scope:
- Claude security vulnerabilities
- API misuse vectors
- Safety system bypasses
contact: "security@anthropic.com"
rewards: "Case by case"
note: "Focus on novel, impactful issues"
Google (Bard/Gemini):
program_type: "Public (Google VRP)"
scope:
- Bard/Gemini vulnerabilities
- AI Studio security
- Model extraction risks
rewards:
critical: "$10,000 - $31,337"
high: "$5,000 - $10,000"
medium: "$1,000 - $5,000"
url: "https://bughunters.google.com/"
Microsoft (Azure AI):
program_type: "Public (MSRC)"
scope:
- Azure OpenAI Service
- Cognitive Services
- ML infrastructure
rewards:
critical: "$15,000 - $26,000"
high: "$5,000 - $15,000"
medium: "$1,000 - $5,000"
url: "https://www.microsoft.com/msrc"
Meta (Llama):
program_type: "Public (Meta Bug Bounty)"
scope:
- Llama model vulnerabilities
- Meta AI products
- API security
rewards:
minimum: "$500"
maximum: "$100,000+"
url: "https://www.facebook.com/whitehat"
class BugBountyPlatforms:
"""Major bug bounty platforms with AI programs."""
PLATFORMS = {
"HackerOne": {
"url": "https://hackerone.com/",
"ai_programs": [
"OpenAI",
"Anthropic",
"Stability AI",
"Character.ai"
],
"features": [
"Managed disclosure",
"Reputation system",
"Payment handling"
],
"researcher_fee": "None"
},
"Bugcrowd": {
"url": "https://bugcrowd.com/",
"ai_programs": [
"Various startups",
"Enterprise AI"
],
"features": [
"Crowdsourced testing",
"Skills-based matching",
"Training resources"
],
"researcher_fee": "None"
},
"Intigriti": {
"url": "https://intigriti.com/",
"ai_programs": [
"European AI companies"
],
"features": [
"GDPR compliant",
"European focus"
],
"researcher_fee": "None"
}
}
class VulnerabilityReport:
"""Professional vulnerability report template."""
TEMPLATE = """
# AI Security Vulnerability Report
## Metadata
- **Report ID:** {report_id}
- **Date:** {date}
- **Researcher:** {researcher_name}
- **Contact:** {contact_info}
## Executive Summary
**Title:** {title}
**Severity:** {severity}
**CVSS Score:** {cvss_score}
**CVSS Vector:** {cvss_vector}
**Category:** {category}
**OWASP LLM:** {owasp_mapping}
## Vulnerability Details
### Description
{description}
### Affected Systems
- **Product:** {product}
- **Version:** {version}
- **Component:** {component}
- **Environment:** {environment}
### Impact Assessment
#### Confidentiality Impact
{confidentiality_impact}
#### Integrity Impact
{integrity_impact}
#### Availability Impact
{availability_impact}
#### Business Impact
{business_impact}
## Reproduction Steps
### Prerequisites
{prerequisites}
### Step-by-Step Instructions
{reproduction_steps}
### Proof of Concept
{poc_code}
### Expected vs Actual Behavior
- **Expected:** {expected_behavior}
- **Actual:** {actual_behavior}
## Evidence
### Screenshots
{screenshots}
### Logs
{logs}
### Video Demonstration
{video_link}
## Suggested Remediation
### Immediate Actions
{immediate_actions}
### Long-term Fix
{long_term_fix}
### Recommended Timeline
{remediation_timeline}
## Additional Information
### Related CVEs
{related_cves}
### References
{references}
### Disclosure Timeline
| Date | Action |
|------|--------|
{timeline_table}
---
**PGP Key:** {pgp_key}
**Encrypted Communication Preferred**
"""
def generate(self, vulnerability_data: dict) -> str:
"""Generate formatted vulnerability report."""
return self.TEMPLATE.format(**vulnerability_data)
DO:
Authorization:
- Get written permission when possible
- Use official bug bounty programs
- Respect scope limitations
- Document authorization
Minimization:
- Access only necessary data
- Don't exfiltrate real user data
- Stop after proving the issue
- Clean up test artifacts
Reporting:
- Report promptly after discovery
- Provide complete reproduction steps
- Suggest remediation
- Maintain confidentiality
Professionalism:
- Communicate respectfully
- Be patient with vendors
- Credit collaborators
- Follow disclosure timelines
DON'T:
Exploitation:
- Access data beyond POC needs
- Pivot to other systems
- Maintain persistent access
- Use vulnerabilities for profit
Disclosure:
- Disclose before patch (usually)
- Threaten or extort vendors
- Sell vulnerabilities (usually)
- Publish user data
Testing:
- Disrupt production services
- Test without authorization
- Social engineer employees
- Physical intrusion
class LegalConsiderations:
"""Legal framework for security research."""
SAFE_HARBOR_POLICIES = {
"US": {
"primary_law": "Computer Fraud and Abuse Act (CFAA)",
"safe_harbor": [
"Bug bounty program terms",
"DOJ Policy for Good Faith Research (2022)",
"Authorized testing agreements"
],
"risks": [
"CFAA prosecution if unauthorized",
"Civil liability possible"
],
"recommendations": [
"Stay within program scope",
"Document authorization",
"Consult lawyer if uncertain"
]
},
"EU": {
"primary_law": "Various national laws",
"safe_harbor": [
"Coordinated disclosure frameworks",
"NIS2 Directive provisions"
],
"considerations": [
"GDPR for any data accessed",
"National cybercrime laws vary"
]
},
"UK": {
"primary_law": "Computer Misuse Act 1990",
"safe_harbor": [
"CMA prosecution guidance",
"Good faith research guidance"
],
"recommendations": [
"Follow NCSC guidance",
"Use authorized programs"
]
}
}
@staticmethod
def assess_legal_risk(jurisdiction: str, research_type: str) -> LegalAssessment:
"""Assess legal risk for security research."""
framework = LegalConsiderations.SAFE_HARBOR_POLICIES.get(jurisdiction)
return LegalAssessment(
jurisdiction=jurisdiction,
safe_harbor_available=bool(framework.get("safe_harbor")),
recommendations=framework.get("recommendations", []),
risks=framework.get("risks", []),
advice="Always seek legal counsel for novel research"
)
Subject: Security Vulnerability Report - [Product Name]
Dear Security Team,
I am a security researcher and I have discovered a vulnerability in
[Product Name] that I would like to report through coordinated disclosure.
**Summary:**
- Type: [Vulnerability Type]
- Severity: [Critical/High/Medium/Low]
- Impact: [Brief impact description]
I am committed to working with you to ensure this issue is resolved
before any public disclosure. My suggested timeline is [X] days,
but I am flexible based on the complexity of the fix.
I have attached a detailed report with reproduction steps. Please
confirm receipt and let me know the best way to proceed.
I am available via encrypted email (PGP key attached) or through
[HackerOne/Bugcrowd/Signal].
Best regards,
[Your Name]
[Contact Information]
[PGP Fingerprint]
Subject: Follow-up: Security Vulnerability Report - [Product Name]
Dear Security Team,
I am following up on my vulnerability report sent on [Date].
I have not received acknowledgment and want to ensure the
report was received.
The vulnerability is [severity level] and affects [scope].
I remain committed to coordinated disclosure but need to
establish communication.
If you are not the correct contact, please forward this to
the appropriate team or provide an alternative contact.
Timeline: [X] days remaining before disclosure deadline.
Best regards,
[Your Name]
AI-Specific Severity Guidelines:
CRITICAL:
examples:
- "Complete safety bypass enabling harmful content at scale"
- "Training data extraction with PII"
- "Remote code execution via model"
- "Authentication bypass to model"
cvss_range: "9.0-10.0"
disclosure_timeline: "45-60 days"
HIGH:
examples:
- "Consistent jailbreak bypass"
- "System prompt extraction"
- "Significant model theft risk"
- "Privilege escalation in agents"
cvss_range: "7.0-8.9"
disclosure_timeline: "60-90 days"
MEDIUM:
examples:
- "Partial information disclosure"
- "Rate limiting bypass"
- "Inconsistent safety bypass"
- "Minor model manipulation"
cvss_range: "4.0-6.9"
disclosure_timeline: "90-120 days"
LOW:
examples:
- "Verbose error messages"
- "Minor configuration issues"
- "Theoretical attacks only"
cvss_range: "0.1-3.9"
disclosure_timeline: "120+ days"
Issue: Vendor not responding
Solution: Try CERT/CC, alternative contacts, consider full disclosure timeline
Issue: Vendor disputes severity
Solution: Provide additional evidence, reference CVSS/OWASP, seek third-party opinion
Issue: Vendor requests extended timeline
Solution: Consider impact, negotiate reasonable extension, document agreement
Issue: Legal threats received
Solution: Consult lawyer, document good faith efforts, contact EFF if needed
| Component | Purpose |
|---|---|
| Agent 01 | Disclosure guidance |
| /report | Report generation |
| HackerOne | Bug bounty submission |
| Legal counsel | Risk assessment |
Practice ethical AI security research through responsible disclosure.