SQL Injection

Prompt注入（Prompt Injection）是针对大语言模型应用的安全威胁，攻击者通过在输入中嵌入恶意指令试图覆盖系统原有行为约束

Prompt Injection is a security attack method targeting AI applications, analogous to SQL injection in traditional web development.

Prompt injection is essentially an architectural flaw in which large language models cannot distinguish data from instructions, analogous to traditional SQL injection in which the database engine confuses data with SQL instructions.

Vulnerabilities such as SQL injection, XSS, and SSRF have long occupied the OWASP Top 10 security risk list.

The principle of SQL injection is that an attacker inserts malicious SQL statement fragments into user input fields; when the backend directly concatenates user input into database query statements, it is executed by the database engine, leading to data leakage, authentication bypass, or even database write operations.

Prompt injection is essentially an instruction hijacking attack that exploits the structural flaw of LLMs being unable to distinguish system instructions from user data at the semantic level, and is conceptually similar to SQL injection.

SQL injection can fundamentally isolate data from instructions through parameterized queries, whereas there is currently no equivalent "parameterized" solution for the LLM inference process.

Large language models cannot reliably distinguish the boundary between system instructions and user/external content during inference—this is a fundamental limitation of prompt injection, similar in principle to SQL injection.

Unlike traditional SQL injection, prompt injection does not rely on syntax vulnerabilities but exploits the large language model's characteristic of treating everything it reads as a potential instruction.

If LLM output is rendered or executed without encoding, it can lead to XSS (when rendered as HTML/Markdown), SQL injection (when concatenated into database queries), and SSRF (when URLs are included for backend fetching).

Prompt injection is harder to defend against than traditional SQL injection, because agents inherently need to understand and execute natural language instructions, making it difficult to distinguish legitimate instructions from malicious injections at the syntactic level.

With the widespread adoption of ORM frameworks and the promotion of parameterized queries, pure SQL injection points are becoming increasingly rare, making logic vulnerabilities relatively more opportunistic.

Prompt injection attempts to override the system prompt and induce the model to perform unauthorized actions by embedding malicious instructions into external content processed by the model, and is logically analogous to SQL injection.

Supabase client built-in methods rather than string-concatenated SQL should be used to prevent SQL injection.

Prompt injection induces agents to perform unauthorized operations by embedding text disguised as instructions within content processed by the agent, operating on a principle similar to SQL injection.

Prompt injection is a class of attack specifically targeting large language models, analogous to SQL injection in traditional web security.

Pattern-based vulnerabilities such as SQL injection and XSS have clear characteristics and relatively standardized remediation approaches that AI models can learn through training data.

Common types of focused web vulnerability training include SQL injection, XSS, privilege escalation vulnerabilities, and front-end JS code auditing.

The logic of prompt injection attacks is highly similar to traditional SQL injection, where attackers embed malicious instructions in input data to induce AI systems to execute data as commands.

Skipping foundational topics such as Linux, web development, and databases to jump straight into tools often yields diminishing returns, as mastering these foundations is a prerequisite for understanding web vulnerabilities and attack principles such as SQL injection.