Why a Classic MCP Server Vulnerability Can Undermine Your Entire AI Agent 2025

Key Takeaways

The supply-chain blast radius is extensive. Anthropic’s vulnerable SQLite MCP server was forked over 5,000 times before being archived. This means this unpatched code now exists inside thousands of downstream agents—many of them likely in production—where it silently inherits SQL-injection risk and any exploit payloads propagate agent-wide.
The SQLite MCP server vulnerability could affect thousands of AI agents. Traditional SQL injection unlocks a new path to stored-prompt injection, enabling attackers to manipulate AI agents directly and dramatically increasing the chances of a successful attack.
The SQL injection vulnerability enables privilege escalation through implicit workflow trust. AI agents often trust internal data whether from databases, log entry, or cached record, agents often treat it as safe. An attacker can exploit this trust by embedding a prompt at that point can later have the agent call powerful tools (e-mail, database, cloud APIs) to steal data or move laterally, all while sidestepping earlier security checks.
No patch is planned, so developers must implement fixes. Organizations using unpatched forks face significant operational and reputational risk, from potential data exposure and service disruption. A list of recommended fixes is provided in the article to help mitigate the vulnerability.

Trend™ Research uncovered a simple but dangerous flaw: a classic SQL injection (SQLi) vulnerability in Anthropic’s reference SQLite Model Context Protocol (MCP) server implementation. Although the GitHub repository was already archived on May 29, 2025, it had already been forked or copied more than 5000 times. It is important to note that the code is clearly advertised as a reference implementation and not intended for production use..

Vulnerable code allows potential attackers to run unauthorized commands, inject malicious prompts, steal data, and hijack AI agent workflows. In this blog entry, we examine where the flaw resides, its impact, and how it can be mitigated.

Where is the vulnerability?

The vulnerability is in how the source code handles user input. It directly concatenates unsanitized user input into an SQL statement which is then later executed by Python’s sqlite3 driver—without filtering or validation. The core SQL injection vulnerability without parameterized input is shown in Figure 1.

Figure 1. The line that causes SQL injection in Anthropic SQLite MCP server source

This flaw creates a serious security risk, specifically the perfect conditions for SQL injection (SQLi), where an attacker can embed malicious queries into the system.

OWASP’s SQL Injection Prevention Cheat Sheet has held the same #1 recommendation for over a decade—use parameterized queries. This prevents SQLi attacks by design, allowing the database to distinguish code from data. Neglecting this best practice reopens the entire catalogue of classic SQL attacks—authentication bypass, data exfiltration, tampering, even arbitrary file writes via SQLite virtual-table tricks.

How a simple SQL injection can hijack a support bot

We illustrate here how this vulnerability can be exploited. In our example, a support AI agent pulls tickets from an SQLite MCP server. Customers submit tickets through a web form, while a privileged support engineer (or bot) triages everything marked open. Figure 2 shows what happens when an attacker slips a SQL injection payload into the support AI agent using a vulnerable SQLite MCP server.

Figure 2. Attack diagram showing how an SQL injection leads to stored prompt injection and data exfiltration in a privileged account.

Here is a step-by-step breakdown of how the attack unfolds:

Attacker submits the ticket

Figure 3 shows the full SQL query when attacker-controlled body field contains an injected SQL statement. A legitimate support ticket request would create an SQL statement ending at line 5. However, the injected content (highlighted in lines 5 to 11) closes the first SQL statement with a fake support ticket entry, and creates a new entry containing a malicious stored prompt.

Figure 3. Normal support ticket would end in line 5. body is controlled by the attacker (highlighted text from line 5 to line 11). The vulnerability allows an extra SQL statement being added.

Malicious prompt is stored via the vulnerable SQLite MCP server

This is the LLM equivalent of a stored XSS attack, commonly called stored prompt injection, a well-known top risk in LLM environments. The vulnerability in SQLite MCP server allows the agent to store malicious prompt in “open” status, effectively bypassing any backend safety guardrail that triages “pending tickets” against prompt injection.

Support engineer or bot triages “open” tickets using AI agent

During triage, through AI agents, a support engineer or bot reads the ticket containing the stored malicious prompts as if it were a valid entry.

The AI model follows instructions in the prompt

The stored prompt steers the LLM into invoking internal tools, such as email client or another MCP server, which is available and allowed in privileged context. In our example, it sends customer data (customer.csv) to an attacker’s email (attacker@evil.com).

Because the email MCP tool operates with elevated privileges and the triage workflow blindly trusts any ticket marked “open,” a single injected prompt combined with stored prompt injection can compromise the system through lateral movement or data exfiltration.

In an AI agent environment that relies on MCP servers to store and retrieve information, a seemingly trivial SQLi bug is no longer just a data-layer bug, but becomes a springboard for stored-prompt injection, steering the agent to execute privileged actions, automate data theft, and perform lateral movement.

Disclosure and current status

On June 11, 2025, we privately reported the issue to Anthropic under responsible-disclosure guidelines. Anthropic replied that the repository is an archived demo implementation, and therefore the vulnerability is considered “out of scope.” As of writing, no patch is planned according to the discussion in GitHub issue #1348. In addition, all Anthropic’s reference implementations under have also been archived.

Quick-Fix checklist

Since no official patch is planned for the archived repository, the task of securing their implementations from this vulnerability falls largely to developers and teams. We have listed here a quick-fix checklist for identifying and patching the vulnerability in developers’ own forks or deployments of the SQLite MCP server:

Fix the vulnerable SQLite MCP server manually by replacing f-strings with placeholders: Check if you forked Anthropic’s sqlite MCP server before May 29, 2025 or if you use any archived version. If found, locate every _execute_query call and enforce using parameterized execution as shown below.


# vulnerable
results = db._execute_query(arguments["query"]) # quick patch
sql, params = arguments["query"], tuple(arguments.get("params", []))
if";" in sql: # simple single-statement guard raise ValueError("stacked queries blocked")
results = db._execute_query(sql, params) # Use parameterized input

Whitelist table names for SELECT … FROM {table} constructs.
Prohibit stacked statements via sqlite3 configuration (isolation_level=None disables implicit autocommit, but you still need input validation).
Monitor for anomalous prompts leaving your LLM boundary (unexpected SELECT * FROM users in generated SQL, or sudden e-mail bursts from the triage agent).

Conclusion and recommendations

This incident reinforces an old lesson with new urgency—classic input-sanitization bugs do not stay confined to the data layer once they sit behind an AI agent. A single, non-parameterized string in an MCP server can cascade through thousands of downstream forks, land in proprietary codebases, and ultimately trigger automated workflows in a privileged context. When an attacker can turn a textbook SQLi into a stored-prompt injection, every subsequent LLM call could be controlled by attacker’s instructions—whether that is reading customer records, firing off e-mails, or pivoting into adjacent systems.

The takeaway is clear. If we allow yesterday’s web-app mistakes to slip into today’s agent infrastructure, we gift attackers an effortless path from SQL injection to full agent compromise. Closing that gap requires disciplined coding standards, automated supply chain monitoring, and runtime guardrails that assume any stored content, regardless of its origin, could be hostile.

In addition to the quick-fix checklist, here are best practices to help prevent vulnerabilities like this one:

Revisit OWASP’s SQL injection prevention cheat sheet. As mentioned earlier, OWASP has long recommended parameterized queries as the #1 defense against SQLi. This should be treated as a foundational practice, especially when integrating with LLMs or agent-based systems.
Audit AI agent workflows for hidden trust assumptions. If AI agents treat internal data as safe by default, it may be vulnerable to stored prompt injection. Review when and how AI reads, interprets, and acts on user-generated or system-stored inputs.
Restrict tool access in privileged contexts. AI agents should not have unrestricted access to tools like email, file systems, or external APIs. Introduce verification steps, approvals, or sandboxing to limit impact in the event of exploitation.
Monitor for anomalies. Keep an eye out for suspicious prompts, unexpected SQL commands, or unusual data flows—like outbound emails triggered by agents outside standard workflows.

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