Incident Response Playbook

Incident Response Playbook

This guide provides step-by-step procedures for responding to AI agent skill security incidents, from initial detection through full recovery.

Incident Types & Severity Levels

Severity Classification

CRITICAL (Red)

  • Active exploitation in production
  • Mass deployment of malicious skills
  • Data exfiltration confirmed
  • System compromise in progress
  • Response time: 1 hour

HIGH (Orange)

  • Confirmed malicious skill found
  • Potential widespread exposure
  • Vulnerability affecting many users
  • Active campaign detected
  • Response time: 4 hours

MEDIUM (Yellow)

  • Suspicious skill behavior detected
  • Potential vulnerability identified
  • Limited user impact
  • Investigation needed
  • Response time: 1 business day

LOW (Blue)

  • Minor security finding
  • No immediate risk
  • Policy violation
  • Documentation issue
  • Response time: 1 week

Incident Response Workflow

Detect
  ↓
Analyze & Classify
  ↓
Notify Stakeholders
  ↓
Contain & Mitigate
  ↓
Investigate
  ↓
Remediate
  ↓
Communicate
  ↓
Post-Incident Review

Playbook 1: Malicious Skill Discovery

Scenario

A malicious skill has been discovered in a registry.

Detection Indicators

  • ✓ Automated scanner flags on installation
  • ✓ User reports suspicious behavior
  • ✓ Manual security review finds issues
  • ✓ Threat intelligence indicates compromise
  • ✓ Unusual network activity detected

Step 1: Initial Response (Immediate - 15 minutes)

1.1 Confirm Detection

# Verify skill details
skill_id="suspicious-skill-123"
platform="openclap"

# Pull skill metadata
curl -s https://api.${platform}.dev/skills/${skill_id} | jq .

# Check download statistics
curl -s https://api.${platform}.dev/skills/${skill_id}/stats | jq .

# Review recent reviews/reports
curl -s https://api.${platform}.dev/skills/${skill_id}/reports | jq .

1.2 Create Incident Ticket

Incident: Malicious Skill Discovery
ID: INC-2026-0001
Severity: HIGH
Skill ID: suspicious-skill-123
Platform: OpenClaw
Discovered: 2026-03-22T10:30:00Z
Reported By: Security Team
Status: OPEN

1.3 Assemble Response Team

  • Security Engineer (Lead)
  • Platform Administrator
  • Threat Intelligence Analyst
  • Communications Officer
  • Legal/Compliance (if needed)

Step 2: Analysis & Classification (30 minutes)

2.1 Analyze Skill Content

# Automated analysis
from ast10_scanner import SkillAnalyzer

analyzer = SkillAnalyzer()

skill_content = download_skill('suspicious-skill-123')
analysis = analyzer.comprehensive_scan(skill_content)

# Check findings
for finding in analysis.vulnerabilities:
    if finding.severity == 'critical':
        print(f"CRITICAL: {finding.description}")
        print(f"Evidence: {finding.evidence}")

2.2 Determine Maliciousness

Indicators of Compromise:

  • Obfuscated or encoded payloads
  • Unauthorized data exfiltration attempts
  • Command injection patterns
  • Credential harvesting code
  • Communication to known C2 servers
  • Process modification/persistence
  • Lateral movement attempts

2.3 Classify Incident

Type: MALWARE
Subtype: Information Stealer
TTPs: Credential Harvesting, Data Exfiltration
Severity: CRITICAL
Estimated Impact: 12,400+ installations

Step 3: Notification & Escalation (30 minutes)

3.1 Notify Internal Teams

TO: Security Operations Center
CC: Executive Team, Legal
PRIORITY: CRITICAL

Subject: CRITICAL - Malicious Skill Found: [skill-id]

Details:
- Skill ID: suspicious-skill-123
- Downloads: 12,400+
- Type: Information Stealer
- Confidence: 95%
- Status: Under Investigation

Actions Initiated:
- Skill flagged for removal
- Platform notifications queued
- Incident response activated

3.2 Notify Platform Operators

Affected Platforms: OpenClaw, Claude Code
Urgency: IMMEDIATE ACTION REQUIRED

The following skill has been identified as malicious:
- ID: suspicious-skill-123
- Classification: Critical Malware
- Estimated Impact: 12,400+ affected users
- Recommended Action: Immediate removal

We request:
1. Immediate skill removal from registry
2. User notifications
3. Download halt
4. User support coordination

Step 4: Containment & Mitigation (1 hour)

4.1 Remove Malicious Skill

# Platform-specific removal
# OpenClaw
claw skill remove suspicious-skill-123 --force --reason "CRITICAL: Malware discovered"

# Claude Code
claude skill remove suspicious-skill-123 --emergency

# Cursor
cursor skill revoke suspicious-skill-123 --immediate

# VS Code
vsce withdraw suspicious-skill-123 --force

4.2 Issue User Alerts

Alert Template:

SECURITY ALERT: Malicious Skill Detected

A skill you may have installed has been identified as malicious:

Skill Name: [name]
Skill ID: suspicious-skill-123
Download Date: [optional]
Risk: HIGH

Actions You Should Take:
1. Uninstall the skill immediately
2. Reset any API keys or credentials
3. Check your data for unauthorized access
4. Report if you've noticed suspicious activity

Steps to Uninstall:
[Platform-specific instructions]

Questions? Contact: [email protected]

4.3 Block Installation

Quarantine Rule:
- Skill ID: suspicious-skill-123
- Reason: Malware detection
- Scan Result: 95% confidence malicious
- Action: Block installation
- Duration: Permanent until reviewed

Step 5: Investigation (2-4 hours)

5.1 Detailed Analysis

# Extract skill components
unzip skill-archive.zip -d /tmp/skill-analysis

# Static analysis
ast10-scan /tmp/skill-analysis --detailed

# Check for obfuscation
entropy-check /tmp/skill-analysis
string-analysis /tmp/skill-analysis

# Network analysis
grep -r "http\|dns\|socket" /tmp/skill-analysis

5.2 Build Indicators of Compromise

{
  "iocs": [
    {
      "type": "domain",
      "value": "malicious.example.com",
      "confidence": "high"
    },
    {
      "type": "ip_address",
      "value": "192.0.2.1",
      "confidence": "high"
    },
    {
      "type": "file_hash",
      "value": "abc123def456...",
      "confidence": "very_high"
    }
  ]
}

5.3 Determine Scope & Timeline

Timeline:
- 2026-01-15: Skill created
- 2026-01-20: First downloads (23 users)
- 2026-02-10: Downloaded 1,200 times
- 2026-03-01: Peak downloads (8,400 total)
- 2026-03-22: Malicious behavior reported

Affected Users: ~12,400
Geographical Distribution: Global
Platforms: OpenClaw (primary), Claude Code (secondary)

Step 6: Remediation (1-3 days)

6.1 User Support

Support Actions:
- [ ] Create FAQ page
- [ ] Set up dedicated support phone line
- [ ] Email affected users with remediation steps
- [ ] Provide credential reset assistance
- [ ] Monitor for follow-up incidents

Resources:
- Credential reset guide: [link]
- FAQ: [link]
- Emergency support: 1-800-SECURITY

6.2 System Cleanup

For affected users:

# Step 1: Stop all agents
systemctl stop agent-service

# Step 2: Remove malicious skill
rm -rf ~/.agent/skills/suspicious-skill-123

# Step 3: Clear caches
rm -rf ~/.agent/cache/*

# Step 4: Reset credentials
generate-new-api-keys

# Step 5: Restart agents
systemctl start agent-service

# Step 6: Verify clean state
agent-security-check

6.3 Infrastructure Hardening

Post-Incident Hardening:
- [ ] Increase automated scanning sensitivity
- [ ] Add skill behavior monitoring
- [ ] Implement stricter code review
- [ ] Deploy behavioral analysis
- [ ] Add user activity monitoring
- [ ] Implement 2FA for publisher accounts

Step 7: Communication (Ongoing)

7.1 Public Statement (Template)

# Security Incident: Malicious Skill Removal

## What Happened
On March 22, 2026, our security team identified a malicious skill
in the OpenClaw registry designed to steal user credentials.

## Impact
- 12,400 installations
- 0 confirmed credential thefts detected
- All affected installations removed

## Actions Taken
- Immediately removed skill from all registries
- Notified all users
- Enhanced detection systems
- Provided remediation guidance

## What You Should Do
1. Check if you installed "suspicious-skill-123"
2. Uninstall if present
3. Reset credentials as precaution
4. Keep software updated

## Timeline
[Detailed timeline of discovery and response]

## Questions?
Contact: [email protected]

7.2 Status Updates

  • Hour 1: Initial notification
  • Hour 4: Removal complete notification
  • Day 1: Detailed incident report
  • Week 1: Post-incident review complete
  • Month 1: Preventive measures deployed

Step 8: Post-Incident Review (1 week)

8.1 Blameless Postmortem

# Incident Postmortem: Malicious Skill INC-2026-0001

## Timeline
[Detailed timeline with all actions]

## Detection Analysis
- How did we detect it?
- Could we have detected earlier?
- What were the warning signs?

## Response Analysis
- What worked well?
- What could be improved?
- Was communication effective?

## Root Cause
[Understanding of how malicious skill was published]

## Preventive Actions
1. [Action to prevent recurrence]
2. [Action to detect faster]
3. [Action to contain better]

## Lessons Learned
[Key insights from incident]

8.2 Process Improvements

Improvements Implemented:
- [ ] Automated behavioral analysis for all skills
- [ ] Real-time threat intelligence integration
- [ ] Enhanced code review procedures
- [ ] Publisher account security hardening
- [ ] User notification automation
- [ ] Faster skill removal workflows

Playbook 2: Data Breach via Skill

Scenario

A skill has been used to exfiltrate sensitive user data.

Response Priorities

  1. Immediate (15 min): Stop data flow, notify affected users
  2. Urgent (1 hour): Contain scope, preserve evidence
  3. Short-term (1 day): Notify authorities, support users
  4. Medium-term (1 week): Full investigation, remediation
  5. Long-term (1 month): Prevention, lessons learned

Critical Actions

# 1. Stop exfiltration
firewall-rule --block destination=attacker-ip --priority=critical

# 2. Isolate affected systems
agent shutdown --affected-users=[list]

# 3. Preserve logs
backup-logs --critical-only /data/incidents/inc-2026-0001

# 4. Revoke compromised credentials
revoke-credentials --all-affected-users

# 5. Notify users
send-incident-notification --template=data_breach --recipients=[list]

Regulatory Compliance

  • Notify regulators within required timeframe (typically 72 hours)
  • Consult legal team
  • Prepare regulatory notifications
  • Document all steps for audit

Playbook 3: Supply Chain Attack Detection

Scenario

Compromised dependencies could propagate malware.

Detection Process

1. Scan dependency tree
2. Check each dependency's integrity
3. Verify signatures
4. Check for known vulnerabilities
5. Analyze behavioral changes

Response

1. Isolate affected versions
2. Notify dependent skills
3. Provide patched versions
4. Update threat intelligence
5. Communicate to users

Escalation Contacts

Internal Escalation

  • Level 1: Security Team Lead
  • Level 2: CISO
  • Level 3: Executive Team / Board

External Notification

Incident Support


Resources & Templates


Playbooks updated: March 2026. Review quarterly and after each major incident.


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Leadership & Founding Members

Project Leadership

Current Leaders

Ken Huang

Ken Huang

Hammad Atta

Hammad Atta

Fabio Cerullo

Fabio Cerullo

Aonan Guan

Aonan Guan

Bhavya Gupta

Bhavya Gupta

Niv Hoffman

Niv Hoffman

Iftach Orr

Iftach Orr

Akram Sheriff

Akram Sheriff

AIVSS Distinguished Review Board

The OWASP AIVSS project’s Distinguished Review Board comprises world-renowned cybersecurity leaders, former government officials, and industry pioneers who provide strategic guidance and expert oversight for the AI Vulnerability Scoring System framework. We thank them for their guidance, several of whom have also supported this project’s work.

Rob Joyce

Rob Joyce

Advisor to PwC and OpenAI, Former Special Assistant to the President and Cybersecurity Coordinator

Jason Clinton

Jason Clinton

Deputy CISO, Anthropic

Amy R. Steagall

Amy R. Steagall

Chief Information Security Officer, Stanford University

Martin Stanley

Martin Stanley

AI Risk Management Framework Lead, NIST

Apostol Vassilev

Apostol Vassilev

Research Supervisor, NIST

Andrew Coyne

Andrew Coyne

CISO, Banner Health, Former CISO, Mayo Clinic

Kevin Rocque

Kevin Rocque

Managing Director/Executive Vice President, Global Technology Risk Officer, TD Bank

Jeff Williams

Jeff Williams

Former Global OWASP Chair, Founder and CTO, Contrast Security

Michael Tran Duff

Michael Tran Duff

University Chief Information Security and Data Privacy Officer, Harvard University

Emil Bender Lassen

Emil Bender Lassen

Standards Lead, AIUC-1

Agentic Skills Top 10 Founding Members

Founding members of the OWASP Agentic Skills Top 10 project itself — project leads, co-leads, and additional contributors — listed alphabetically. Several also contribute to the sibling OWASP AIVSS project listed above.

Ken Huang

Ken Huang

Project Lead, Agentic Skills Top 10

Hammad Atta

Hammad Atta

Co-Lead, Agentic Skills Top 10

Manish Bhatt

Manish Bhatt

Security Researcher, AWS

Fabio Cerullo

Fabio Cerullo

Co-Lead, Agentic Skills Top 10

David Girard

David Girard

Senior Director, AI Security & AI Alliances, Trend Micro

Aonan Guan

Aonan Guan

Co-Lead, Agentic Skills Top 10

Bhavya Gupta

Bhavya Gupta

Co-Lead, Agentic Skills Top 10

Pamela Gupta

Pamela Gupta

Founder & CEO, OutSecure / Trusted AI

Idan Habler

Idan Habler

Staff AI/ML Security Researcher, Intuit

Niv Hoffman

Niv Hoffman

CTO, Air Security

Charles Iheagwara

Charles Iheagwara

AI/ML Security Leader, AstraZeneca

Sushmitha Janapareddy

Sushmitha Janapareddy

Director - Security Integrations, American Express

Edward Lee

Edward Lee

Vice President, Lead AI Security, JP Morgan

KJ Lian

KJ Lian

Senior Manager, Data & AI (Public Sector), AWS

Vineeth Sai Narajala

Vineeth Sai Narajala

Application Security, AWS

Iftach Orr

Iftach Orr

Co-Lead, Agentic Skills Top 10

Kanna Sekar

Kanna Sekar

Cyber Security, Google

Akram Sheriff

Akram Sheriff

Co-Lead, Agentic Skills Top 10

Dennis Xu

Dennis Xu

Research VP, AI, Gartner

OWASP AIVSS Founding Members

The OWASP AIVSS (Agentic AI Vulnerability Scoring System) project is a sibling OWASP initiative focused on scoring the severity of agentic AI vulnerabilities. Its founding members are recognized here as OWASP founding members in the agentic AI security space; many of them have also contributed directly to the Agentic Skills Top 10 project’s research and review process.

Sunil Agrawal

Sunil Agrawal

Chief Information Security Officer, Glean

David Ames

David Ames

Partner, PwC

Michael Bargury

Michael Bargury

Founder and CTO, Zenity

Joshua Beck

Joshua Beck

Application Security Architect, SAS

Manish Bhatt

Manish Bhatt

Security Researcher, Amazon Kuiper Security

Mark Breitenbach

Mark Breitenbach

Security Engineer, Dropbox

Anat Bremler-Barr

Anat Bremler-Barr

Professor of Computer Science, Tel Aviv University

Siah Burke

Siah Burke

HIPAA Security Officer, Siah.ai

David Campbell

David Campbell

AI Security, Scale AI

Ying-Jung Chen

Ying-Jung Chen

AI safety researcher, PhD, Georgia Institute of Technology

Anton Chuvakin

Anton Chuvakin

Security Solution Strategy, Google

Jason Clinton

Jason Clinton

CISO, Anthorphic

Adam Dawson

Adam Dawson

Staff AI Security Researcher, Dreadnode

Leon Derczynski

Leon Derczynski

Principal Research Scientist, NVIDIA

Walker Lee Dimon

Walker Lee Dimon

AI Security Researcher, MITRE

Marissa Dotter

Marissa Dotter

AI Security Researcher, MITRE

Dan Goldberg

Dan Goldberg

ISO Market Lead, Omnicom

David Haber

David Haber

CEO, Lakera

Idan Habler

Idan Habler

Staff AI/ML Security Researcher, Intuit

Jason Haddix

Jason Haddix

Founder, Arcanum Information Security

Keith Hoodlet

Keith Hoodlet

Director of AI/ML & AppSec, Trail of Bits

Ken Huang

Ken Huang

AIVSS Project Lead, OWASP

Chris Hughes

Chris Hughes

CEO, Aquia

Charles Iheagwara

Charles Iheagwara

AI/ML Security Leader, AstraZeneca

Krystal Jackson

Krystal Jackson

Researcher, Center for Long-Term Cybersecurity, UC Berkeley

Sushmitha Janapareddy

Sushmitha Janapareddy

Director - Security Integrations, American Express

Rob Joyce

Rob Joyce

Former Cybersecurity Director of NSA, Advisor to PwC, PwC

Diana Kelley

Diana Kelley

CISO, Noma Security

Prashant Kulkarni

Prashant Kulkarni

Lead AI Security Research Engineer, Google Cloud

Mahesh Lambe

Mahesh Lambe

Founder, MIT, Unify Dynamics

Edward Lee

Edward Lee

Vice President, Lead AI Security, JP Morgan

Nate Lee

Nate Lee

CEO, Cloudsec.ai

Vishwas Manral

Vishwas Manral

CEO, Precize.ai

Daniela Muhaj

Daniela Muhaj

Executive-in-Residence for Research & Development, AI 2030

Vineeth Sai Narajala

Vineeth Sai Narajala

Application Security, AWS

Om Narayan

Om Narayan

AI Security Researcher, AWS

Varun Pant

Varun Pant

Engineering and Product Leader, AI applications at the Automated Reasoning Group, AWS

Advait Patel

Advait Patel

Senior Site Reliability Engineer (DevSecOps + Cloud + AIOps), Broadcom, IEEE

Alex Polyakov

Alex Polyakov

CEO, adversa.ai

Ramesh Raskar

Ramesh Raskar

Professor & Director, MIT Media Lab

Ron F. Del Rosario

Ron F. Del Rosario

VP-Head of AI Security, SAP

Tal Shapira

Tal Shapira

Co-Founder & CTO, Reco AI

Akram Sheriff

Akram Sheriff

Senior AI/ML Software Engineering Leader, Cisco

Samantha Siau

Samantha Siau

Security and Compliance, Anthropic

Kevin Simmonds

Kevin Simmonds

Partner on AI Offensive Security, PWC

Martin Stanley

Martin Stanley

NIST AI RMF Lead, Independent

Omar A. Turner

Omar A. Turner

General Manager of Security, Microsoft

Apostol Vassilev

Apostol Vassilev

AI Research Team Supervisor, NIST

Matthew Versaggi

Matthew Versaggi

AI Fellow, White House Presidential Innovation Fellow

David Webb

David Webb

Agency Cybersecurity Officer, Cybersecurity and Infrastructure Security Agency

Dennis Xu

Dennis Xu

Research VP, AI, Gartner

Xiaochen Zhang

Xiaochen Zhang

Executive Director and Chief Responsible AI Officer, AI 2030

Recognition

We extend our gratitude to all founding members who have contributed to establishing this crucial framework for AI security assessment. Their vision and dedication have been instrumental in shaping the Agentic Skills Top 10 project.

Get Involved

Interested in contributing to the Agentic Skills Top 10 project? We welcome new contributors and leaders. Please see our Contribution Guidelines for more information on how to get involved.