Executive Summary
In April 2026, Bishop Fox introduced 'Otto Support,' a Capture-The-Flag (CTF) challenge designed to expose vulnerabilities in Model Context Protocol (MCP)-based AI systems. This hands-on exercise simulates real-world attack scenarios where AI assistants interact with tools, services, and local resources, highlighting potential security flaws in modern AI architectures. Participants are tasked with escalating privileges, exfiltrating data, and executing code, thereby uncovering how MCP-enabled systems can be exploited in practice.
The relevance of this challenge is underscored by the rapid adoption of AI technologies and the corresponding emergence of new attack surfaces. As organizations integrate AI assistants into their operations, understanding and mitigating the security risks associated with MCP-based systems becomes imperative to prevent potential breaches and maintain trust in AI-driven processes.
Why This Matters Now
The rapid integration of AI assistants into business operations has introduced new attack surfaces, particularly in MCP-based systems. Understanding and mitigating these vulnerabilities is crucial to prevent potential breaches and maintain trust in AI-driven processes.
Attack Path Analysis
An attacker exploited vulnerabilities in the Otto Support MCP server to gain initial access, escalated privileges by manipulating AI agent interactions, moved laterally within the system by abusing internal services, established command and control through compromised AI agents, exfiltrated sensitive data via unauthorized tool executions, and caused impact by executing arbitrary code on the underlying system.
Kill Chain Progression
Initial Compromise
Description
The attacker exploited vulnerabilities in the Otto Support MCP server to gain unauthorized access.
Related CVEs
CVE-2025-49596
CVSS 9.4MCP Inspector versions below 0.14.1 lack authentication between the client and proxy, allowing unauthenticated requests to execute MCP commands over stdio, leading to potential remote code execution.
Affected Products:
Model Context Protocol MCP Inspector – < 0.14.1
Exploit Status:
proof of conceptCVE-2026-22708
CVSS 9.8Cursor versions prior to 2.3, when running in Auto-Run Mode with Allowlist mode enabled, allow certain shell built-ins to execute without appearing in the allowlist and without user approval, enabling attackers to manipulate the shell environment via prompt injection.
Affected Products:
Anysphere Cursor – < 2.3
Exploit Status:
no public exploit
MITRE ATT&CK® Techniques
Exploitation for Defense Evasion
Obtain Capabilities: Artificial Intelligence
Phishing
Indicator Removal on Host
Exploitation for Client Execution
Process Injection
Data Encrypted for Impact
Potential Compliance Exposure
Mapping incident impact across multiple compliance frameworks.
EU AI Act – Data Governance
Control ID: Article 15
ISO/IEC 42001 – Risk Assessment
Control ID: 6.2.1
NIST AI RMF – Secure Development Practices
Control ID: 2.3
GDPR – Data Protection by Design and by Default
Control ID: Article 25
NIS2 Directive – Cybersecurity Risk Management Measures
Control ID: Article 21
Sector Implications
Industry-specific impact of the vulnerabilities, including operational, regulatory, and cloud security risks.
Computer Software/Engineering
Critical exposure through AI-enabled development environments where MCP servers access source code, credentials, and internal systems enabling privilege escalation and data exfiltration.
Information Technology/IT
High risk from agentic AI systems with local resource access, creating new attack surfaces through tool abuse and prompt injection in enterprise environments.
Computer/Network Security
Significant impact as security teams must address novel MCP-based vulnerabilities, drive-by localhost attacks, and AI agent framework exploitation requiring new defensive strategies.
Financial Services
Substantial risk from AI assistants accessing sensitive financial data and systems, with HIPAA/PCI compliance violations through east-west traffic and egress security failures.
Sources
- Otto Support – An MCP, Agentic-AI Security Challengehttps://bishopfox.com/blog/otto-support-an-mcp-agentic-ai-security-challengeVerified
- NVD - CVE-2025-49596https://nvd.nist.gov/vuln/detail/CVE-2025-49596Verified
- NVD - CVE-2026-22708https://nvd.nist.gov/vuln/detail/CVE-2026-22708Verified
Frequently Asked Questions
Cloud Native Security Fabric Mitigations and ControlsCNSF
Aviatrix Zero Trust CNSF is pertinent to this incident as it could have constrained the attacker's ability to exploit vulnerabilities, escalate privileges, move laterally, establish command and control, exfiltrate data, and execute arbitrary code, thereby reducing the overall blast radius.
Control: Cloud Native Security Fabric (CNSF)
Mitigation: Implementing Aviatrix CNSF could have limited the attacker's ability to exploit server vulnerabilities by enforcing strict access controls and segmenting network traffic.
Control: Zero Trust Segmentation
Mitigation: Zero Trust Segmentation could have restricted the attacker's ability to escalate privileges by enforcing least-privilege access and segmenting workloads.
Control: East-West Traffic Security
Mitigation: East-West Traffic Security could have constrained the attacker's lateral movement by monitoring and controlling internal traffic flows.
Control: Multicloud Visibility & Control
Mitigation: Multicloud Visibility & Control could have limited the attacker's ability to establish command and control by providing comprehensive monitoring and management across cloud environments.
Control: Egress Security & Policy Enforcement
Mitigation: Egress Security & Policy Enforcement could have restricted the attacker's data exfiltration efforts by controlling outbound traffic and enforcing strict egress policies.
While prior controls may have constrained earlier attack stages, the execution of arbitrary code could still pose risks to system integrity and availability.
Impact at a Glance
Affected Business Functions
- Customer Support Operations
- Internal IT Services
Estimated downtime: 3 days
Estimated loss: $50,000
Potential exposure of customer support tickets and internal system configurations.
Recommended Actions
Key Takeaways & Next Steps
- • Implement Zero Trust Segmentation to restrict AI agent interactions and limit lateral movement.
- • Enforce East-West Traffic Security to monitor and control internal communications, preventing unauthorized access.
- • Deploy Egress Security & Policy Enforcement to control outbound traffic and prevent data exfiltration.
- • Utilize Threat Detection & Anomaly Response to identify and respond to suspicious AI agent behaviors.
- • Apply Inline IPS (Suricata) to detect and prevent exploitation attempts targeting AI agent vulnerabilities.
