June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)
[ AI RED TEAMING · MCP ]
Model Context Protocol has become the backbone of agentic AI infrastructure — and a new attack surface that traditional security approaches cannot adequately address. Adversa AI red teams it continuously.
[ WHY MCP RED TEAMING ]
Introduced by Anthropic and rapidly adopted across the industry, MCP has revolutionized how AI systems connect to external data and tools. That rapid adoption opens an attack surface specialized security testing must address.
Attackers manipulate an agent's objectives or decision-making, causing it to pursue unintended goals — performing unauthorized actions, accessing restricted resources, or circumventing its intended purpose entirely.
MCP servers can inadvertently expose sensitive data across organizational boundaries. Prompt injection, conversation-history access, and credential exposure exfiltrate IP, customer data, and tokens across tenants sharing infrastructure.
Hidden malicious instructions embedded in tool descriptions, schemas, or resources hijack agent behavior — including dynamic tool mutation ("rug pull" attacks) and tool shadowing, where malicious tools override legitimate ones.
MCP's trust model enables sophisticated attacks with no user interaction. By manipulating the RAG pipeline and mixing trusted with untrusted data, attackers trigger autonomous data exfiltration and system compromise.
Command injection, SQL-injection escalation, and DNS-rebinding attacks allow complete system takeover. Unsanitized inputs reaching execution functions let attackers install backdoors and gain persistent access.
Weak OAuth implementations, excessive permission scopes, and session-management flaws enable privilege escalation. Stolen tokens are reused on rogue MCP instances; missing auth guidance leaves endpoints unprotected.
Architectural issues — long-lived TCP connections that can't be monitored, mixed trust boundaries, and insufficient security standards — create systemic vulnerabilities affecting every MCP implementation regardless of vendor.
[ REAL-WORLD INCIDENTS ]
Asana's MCP server exposed data between organizations for over a month, affecting 1,000 customers. A logic flaw let users access tasks, projects, and files from other companies. (June 2025)
Researchers showed how malicious support tickets could exploit Atlassian's MCP implementation, gaining access to internal tenant data and acting as a proxy through support engineers. (June 2025)
A critical vulnerability (CVE-2025-49596, CVSS 9.4) exposed developer machines to remote code execution. Attackers could exploit the MCP Inspector through malicious websites, highlighting risks in MCP tooling.
Security experts warned that MCP servers use "long-lived TCP connections that can't really be monitored" — a "huge, massive attack vector" when connected to critical systems such as SIEMs.
[ SOLUTION ]
We combine automated vulnerability discovery with expert analysis based on real-world incidents, so you can adopt MCP safely while staying resilient against current and emerging threats.
Advanced risk profiling across your MCP deployment, covering:
Continuous automated scanning for:
Sophisticated attack simulations including:
[ GET A DEMO ]
Book a demo of our AI red teaming platform for MCP and discuss your unique challenges with our team.
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No data access required. We red-team on your infrastructure, your way.
