python: Add query for prompt injection

This pull request introduces a new CodeQL query for detecting prompt injection vulnerabilities in Python code targeting AI prompting APIs such as agents and openai. The changes includes a new experimental query, new taint flow and type models, a customizable dataflow configuration, documentation, and comprehensive test coverage.

python/ql/src/experimental/Security/CWE-1427/PromptInjection.qhelp

@@ -0,0 +1,24 @@
+<!DOCTYPE qhelp PUBLIC
+  "-//Semmle//qhelp//EN"
+  "qhelp.dtd">
+<qhelp>
+
+<overview>
+<p>Prompts can be constructed to bypass the original purposes of an agent and lead to sensitive data leak or 
+operations that were not intended.</p>
+</overview>
+
+<recommendation>
+<p>Sanitize user input and also avoid using user input in developer or system level prompts.</p>
+</recommendation>
+
+<example>
+<p>In the following examples, the cases marked GOOD show secure prompt construction; whereas in the case marked BAD they may be susceptible to prompt injection.</p>
+<sample src="examples/example.py" />
+</example>
+
+<references>
+<li>OpenAI: <a href="https://openai.github.io/openai-guardrails-python">Guardrails</a>.</li>
+</references>
+
+</qhelp>

python/ql/src/experimental/Security/CWE-1427/PromptInjection.ql

@@ -0,0 +1,20 @@
+/**
+ * @name Prompt injection
+ * @kind path-problem
+ * @problem.severity error
+ * @security-severity 5.0
+ * @precision high
+ * @id py/prompt-injection
+ * @tags security
+ *       experimental
+ *       external/cwe/cwe-1427
+ */
+
+import python
+import experimental.semmle.python.security.dataflow.PromptInjectionQuery
+import PromptInjectionFlow::PathGraph
+
+from PromptInjectionFlow::PathNode source, PromptInjectionFlow::PathNode sink
+where PromptInjectionFlow::flowPath(source, sink)
+select sink.getNode(), source, sink, "This prompt construction depends on a $@.", source.getNode(),
+  "user-provided value"

python/ql/src/experimental/Security/CWE-1427/examples/example.py

@@ -0,0 +1,17 @@
+from flask import Flask, request
+from agents import Agent
+from guardrails import GuardrailAgent
+
+@app.route("/parameter-route")
+def get_input():
+    input = request.args.get("input")
+
+    goodAgent = GuardrailAgent(  # GOOD: Agent created with guardrails automatically configured.
+        config=Path("guardrails_config.json"),
+        name="Assistant",
+        instructions="This prompt is customized for " + input)
+
+    badAgent = Agent(
+        name="Assistant",
+        instructions="This prompt is customized for " + input  # BAD: user input in agent instruction.
+    )