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Status as of July 12, 2026: Research by TrendAI’s Forward-Looking Threat Research Team (Vincenzo Ciancaglini, Marco Balduzzi, Alfredo Oliveira, and David Fiser), published June 24, 2026. Covered by security press from July 7 onward. No CVEs are assigned to individual servers in this study; the findings are systemic, not targeted to specific named products.

The Study in One Paragraph

TrendAI analyzed 9,695 unique MCP servers crawled from four directories — GitHub, Glama, Lobehub, and PulseMCP — between November 2025 and March 2026. They correlated directory metadata (star count, commit activity, verification status) with security issues identified through vulnerability scanning. Finding: 5,832 servers had at least one security weakness (60.2% of the sample); 2,259 servers had exploitable vulnerabilities beyond missing authentication. Those 2,259 servers contained 4,982 documented security problems. The trust signals developers actually use — stars, badges, commits — showed no statistically meaningful correlation with security posture.

The Numbers

Vulnerability Class Count
Missing authentication 2,054
Arbitrary file access 880
Denial of service 490
Command injection 476
SSRF 422
SQL injection 211
Prompt injection 185
Cross-site scripting 155
Code injection 101
Authorization bypass 8
Total issues 4,982

Two things stand out in this table. First, the raw authentication gap is enormous: 2,054 of the analyzed servers accept requests from anyone, no credential check required. This is not a new finding — the NSA and OWASP both flagged the authentication problem in 2026 — but the scale here is striking. Second, the exploitable vulnerability classes are clustered where developer shortcuts happen: arbitrary file access (handlers that take user input and pass it into filesystem operations), command injection (handlers that interpolate input into shell calls), and SSRF (handlers that fetch remote URLs without allowlist validation). These are not sophisticated zero-days. They are the same class of bugs that have appeared in web apps for twenty years, now appearing in MCP server implementations written under time pressure by developers focused on functionality rather than security.

The Counterintuitive Finding: Social Signals Are Blind to Security

The study’s central contribution is not the vulnerability count — it is what it reveals about how developers currently evaluate MCP server safety.

The working assumption in most teams is that a popular, actively maintained, verified MCP server is probably safe enough to integrate. The TrendAI data directly challenges this:

GitHub stars: “The average number of security issues per server does not change significantly” across popularity tiers. A server with 200 stars has approximately the same expected issue count as one with 5. High star counts do not filter for security practice; they filter for discoverability and usefulness. When a popular server has a vulnerability, the blast radius is larger — more integrations, more exposed environments — but the presence of stars provides no predictive signal about whether a vulnerability exists.

Commit activity: Commit counts similarly showed “little to no correlation across actively developed tiers.” Active development indicates that a project’s features are being maintained. It does not indicate that new contributions are being reviewed for security, that dependencies are being audited, or that newly introduced input-handling code is safe. In fact, active feature development in immature projects often introduces new attack surface faster than it eliminates old bugs.

Verification badges: Directory verification, across the registries studied, certifies that a repository exists and is reachable — not that its code is safe. “No significant difference in the average number of issues per server between verified and unverified repositories” emerged from the analysis. The badge is a curation signal, not a security signal, and treating it as the latter is an organizational risk.

The TrendAI paper titles this finding “Stars Don’t Save You.” The implication for teams doing MCP server evaluation: your existing shortcut (“check the stars, check the badge, check the recent commits”) is producing a false sense of assessed risk.

Three Real-World Vulnerability Profiles

TrendAI organized their findings by provider profile rather than by CVE, because the vulnerabilities were not in isolated libraries but in patterns of how developers implement MCP tool handlers. Three profiles appeared frequently enough to warrant case studies:

Cryptocurrency and DeFi Providers

Forty or more cryptocurrency-related MCP servers were released without directory verification and without standard security review. The specific vulnerability classes found: server-side template injection and prompt injection. The template injection bugs risked “full server-side code execution” — a compromised MCP server becomes a remote shell. The prompt injection vulnerabilities could redirect trading agents: an attacker able to inject into the context of a DeFi integration could manipulate what the agent perceives as legitimate instructions, potentially triggering unauthorized transactions.

Office Automation Providers

MCP servers handling document creation, spreadsheet manipulation, and calendar automation contained direct eval() calls on user-supplied input. This is as direct as code injection gets: the handler takes text, evaluates it as Python, and returns the result. Servers in this category had 100+ GitHub stars — well above the average — and the eval() patterns were in recently committed code. The path traversal vulnerabilities in these servers allowed traversal outside the intended document directory.

Enterprise Database Middleware

Commercial middleware vendors offering MCP interfaces to corporate databases — including systems connected to financial and HR records — had SQL injection vulnerabilities in their query handlers and unauthenticated Active Directory query access. Unauthenticated AD access enables enumeration of users, groups, and organizational structure — reconnaissance that directly supports privilege escalation in downstream attacks. These were not hobby projects; they were enterprise products from vendors who had gone through directory verification.

Why This Happens

The TrendAI team notes that “most vulnerabilities were categorized as developer-introduced flaws rather than intentional malicious behavior.” The supply chain threat here is not primarily from adversarial MCP servers (though MCPTox documents that attack class); it is from well-intentioned developers who implemented tool handlers without security training on this specific problem surface.

MCP tool handlers are a new class of server-side code. The patterns that make them exploitable — taking agent-provided strings and passing them unsanitized into shell execution, file system calls, SQL queries, or network requests — are identical to the patterns that have made web app code exploitable for decades. But MCP developers do not typically have web application security backgrounds, and there is no equivalent of OWASP Top 10 guidance that has been integrated into MCP documentation and tooling. The OWASP MCP Top 10 exists as a standalone list, but it is not embedded in SDK documentation or surfaced in development workflows.

The co-occurrence patterns TrendAI identified reinforce the systemic nature: servers with arbitrary file access tend to also have missing authentication. These vulnerabilities are not appearing in isolation; they are appearing together because they flow from the same root cause — a development process without security gates.

What This Means for Your MCP Integration

The study does not tell you which specific servers to avoid. It tells you that the signals you are currently using to make that decision are not reliable. The operational implication: treat every third-party MCP server as unvetted code until you have reviewed it yourself, regardless of star count, verification status, or commit activity.

That review does not have to be comprehensive to be useful. The vulnerability classes TrendAI found are largely discoverable by pattern:

Check for unsanitized input flows. Look for tool handlers that take parameters and pass them to subprocess.run, os.system, exec, eval, open, or SQL string interpolation without validation or allowlisting. Any of these patterns is a candidate vulnerability.

Check authentication. Does the server require any credential before accepting tool calls? Is that credential validated per-request or only at connection time? Is there a per-tool permission model, or is it all-or-nothing?

Check the scope of file access. If the server has filesystem tools, does it enforce a root directory boundary? Can a relative path like ../../.ssh/authorized_keys traverse outside the intended scope?

Check network request handling. If the server fetches external URLs, does it have an allowlist? Can an agent-supplied URL cause the server to fetch internal infrastructure addresses (SSRF)?

These questions take thirty minutes to answer for a simple MCP server. For complex servers, automated scanning tools — the VIPER-MCP framework being the most capable currently available — can cover more ground. The MCP-DPT taxonomy identifies which defense tools cover which vulnerability classes if you want to instrument your own integrations.

For enterprise teams with multiple MCP integrations, TrendAI recommends:

  • Code audits before any production integration, not just star-checking
  • Enforced authentication and least-privilege configuration for every server
  • Rigorous input validation requirements as a condition of integration approval
  • Real-time monitoring of agent-MCP traffic for behavioral anomalies
  • Treating third-party MCP servers as unvetted code, not as trusted libraries

The Bigger Picture

The MCP security research landscape is now large enough to see a pattern. VIPER-MCP found 106 zero-days in 39,884 open-source repos. The NSA Cybersecurity Information Sheet flagged the authentication gap. MCPTox documented how tool descriptions can be poisoned against any model. ShareLock found OAuth delegation gaps. AgentJacking showed how agents can be redirected into malicious execution paths. SMCP proposed protocol-level fixes for the identity and authentication problems. MCP-DPT mapped which defenses actually cover which attacks.

What the TrendAI study adds to this picture is the ecosystem dimension: it is not a handful of vulnerable servers identified through targeted research. It is 60% of a large, broad sample, with no meaningful difference between the servers developers currently trust and the ones they do not. The security gap is not concentrated in the obvious low-quality projects. It is distributed across the ecosystem, in popular servers, in verified servers, in actively maintained servers.

The practical consequence: the MCP ecosystem is currently at a stage where integration risk cannot be delegated to social signals. Every integration requires its own review.

Original research: TrendAI Forward-Looking Threat Research Team (Vincenzo Ciancaglini, Marco Balduzzi, Alfredo Oliveira, David Fiser). “Stars Don’t Save You: Popularity Is Not Security in the MCP Ecosystem.” Published June 24, 2026. trendaisecurity.com

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