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SQL injection targets how a database interprets a query; prompt injection targets how an AI system interprets instructions and content. Both involve untrusted input crossing a trust boundary, but the attack mechanisms and defenses are different.
What is SQL injection?
SQL injection occurs when an application incorporates untrusted input into a database query in a way that lets the input change the query’s syntax or intent. NIST’s glossary defines it as “Attacks that look for web sites that pass insufficiently-processed user input to database back-ends.” NIST’s SQL injection glossary entry cites NISTIR 7682 for that definition.
A common flaw is building a dynamic SQL statement by concatenating user input into a string. If the database parses part of that input as SQL syntax rather than as a value, the resulting query can behave differently than the application intended. Depending on the vulnerable query and the application’s database permissions, that may expose or modify data. OWASP’s SQL Injection Prevention Cheat Sheet describes this string-concatenation pattern and recommended controls.
What is prompt injection?
Prompt injection is an attempt to influence how an AI application follows instructions or handles content by placing malicious or untrusted text in the model’s context. NIST’s AI 100-2e2025 glossary defines it as “An attack which exploits the concatenation of untrusted input with a prompt constructed by a higher-trust party such as the application designer.” NIST’s prompt injection glossary entry explains the trust-boundary issue behind the term.
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OWASP notes that many large language model applications process natural-language instructions and data together without a clear separation. An attack can be direct, in a user’s prompt, or indirect: malicious instructions may be embedded in a webpage, document, or email that the AI is asked to read. The model may mistake that content for instructions to follow. OWASP’s LLM Prompt Injection Prevention Cheat Sheet discusses this design risk, while Microsoft’s Prompt Shields guidance gives examples involving external content.
Prompt injection does not automatically mean an attacker can reach private data or execute an action. The possible impact depends on what information the application provides to the model and which tools or permissions the model can use.
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How the attacks differ
| Comparison | SQL injection | Prompt injection |
|---|---|---|
| What it targets | Interpretation of a database query. | Interpretation of instructions and content by an AI model or agent. |
| Typical entry point | Untrusted input incorporated into a dynamically constructed query. | Text supplied directly by a user or indirectly through content the AI reads, such as a webpage, document, or email. |
| How it can fail | Input changes query structure or intent, potentially exposing or modifying data. | Adversarial content changes model behavior; in a connected application, that may affect data access or actions, depending on permissions and tools. |
| Main defensive approach | Use parameterized queries or prepared statements; allow-list structural choices that cannot be bound as ordinary values. | Maintain trust boundaries, limit permissions and tool access, review consequential actions, and test the application against adversarial input. |
How to prevent SQL injection
Use parameterized queries for values
Prepared statements with variable binding keep SQL code separate from the values supplied to it. The database treats bound input as data rather than parsing it as part of the query’s structure. This is OWASP’s primary recommended defense for SQL injection.
Allow-list structural choices
Some query elements, such as a table name, column name, or sort direction, generally cannot be supplied as ordinary bound values. Prefer choosing these elements in application code. If users must select them, map their choices to a fixed set of permitted values rather than inserting arbitrary input into the SQL string.
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Do not rely on escaping as the main fix
Escaping all user input is fragile and database-specific. OWASP does not recommend it as a general primary defense in place of parameterization and safe query construction.
How to reduce prompt-injection risk
Keep untrusted content distinct
Applications should identify external or user-provided material as untrusted data rather than letting it blend indistinguishably with higher-trust instructions. This can help systems handle retrieved text as content to analyze, not as authority to override the task.
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Limit what the model can access and do
Give an AI system only the data, tools, and permissions needed for its task. Constrain tool capabilities and avoid granting broad discretion. If a prompt injection succeeds in influencing the model, least privilege can limit the resulting impact.
Review consequential actions
Require human review or approval before privileged or consequential operations are confirmed. OpenAI’s agent safety guidance advises limiting agent access, providing specific instructions, and reviewing consequential actions.
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Test the whole application
Test how the model, retrieved content, connected tools, and approval steps behave together under adversarial input. Prompt wording or a pattern filter alone cannot guarantee prevention. OWASP cautions that “there is no fool-proof prevention within the LLM”; its guidance focuses on mitigating impact through broader system controls. OWASP’s LLM01: Prompt Injection guidance provides further mitigation advice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Are SQL injection and prompt injection the same thing?
No. Calling prompt injection “SQL injection for AI” is a shorthand, not a precise technical description. The analogy is useful only at the level of trust boundaries: in both cases, untrusted material can influence a higher-trust processing context. SQL injection changes how a database parses a query; prompt injection exploits how an AI system interprets natural-language instructions and data. Their payloads, interpreters, possible impacts, and primary defenses differ.
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