no-untrusted-content-in-prompt

Disallow untrusted content built into the Gemini system prompt.

• CWE: CWE-1427 — Improper Neutralization of Input Used for LLM Prompting
• OWASP: A03:2021 — Injection · LLM Top 10: LLM01 Prompt Injection
• CVSS: 8.1 (High) · Preset: strict only

Why

A system prompt is instruction text. Whatever is spliced into it is read by the model as instructions, not as data — so anyone who controls that value controls the agent's rules. The model has no way to tell "this part of my instructions came from a stranger" once the string is assembled.

This is the same argument as mcp-sdk-security/no-tool-description-injection, one layer down: tool descriptions and system prompts are both model-facing instruction surfaces, and both must be static.

What counts as static

A string literal, a template with no interpolations, or a concatenation of those. A bare identifier also counts — systemInstruction: SYSTEM_PROMPT is the correct pattern and by far the most common one, and following it is the data-flow analysis this rule deliberately avoids. The interpolation is where the injection is visible in one place, and that is what gets reported.

Not this rule

• The Vercel AI SDK. generateText({ system }) and streamText({ system }) belong to vercel-ai-security/no-dynamic-system-prompt. This rule gates on Gemini's own request paths, so no line is ever reported by both.
• User turns. { role: 'user', content: `${input}` } is the remediation, not the bug — a user message is where runtime values belong, because the model reads them as data.

Known limitations

• The gate is file-level: once the Gemini SDK is imported anywhere in the file, any call whose member path matches a request path is inspected. An unrelated object that happens to expose the same path — ai.models.generateContent on something that is not a Gemini client — is a false positive. Resolving the callee back to the import would need the cross-scope data-flow analysis these rules deliberately avoid; the trade is a rule that stays fast and predictable, and it is why this rule ships in strict only until the corpus run measures the real rate.
• A system prompt assembled before the call (const p = base + role; then systemInstruction: p) is a false negative, for the same reason a bare identifier counts as static.

Incorrect

await ai.models.generateContent({ systemInstruction: `Answer as ${persona}.`, contents });

Correct

await ai.models.generateContent({
  systemInstruction: 'You are a helpful assistant.',
  contents: [{ role: 'user', parts: [{ text: `Persona: ${persona}` }] }],
});