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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.
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‎python/ql/integration-tests/query-suite/not_included_in_qls.expected‎

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ql/python/ql/src/experimental/Security/CWE-091/XsltInjection.ql
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ql/python/ql/src/experimental/Security/CWE-094/Js2Py.ql
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ql/python/ql/src/experimental/Security/CWE-1236/CsvInjection.ql
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ql/python/ql/src/experimental/Security/CWE-1427/PromptInjection.ql
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ql/python/ql/src/experimental/Security/CWE-176/UnicodeBypassValidation.ql
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ql/python/ql/src/experimental/Security/CWE-208/TimingAttackAgainstHash/PossibleTimingAttackAgainstHash.ql
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ql/python/ql/src/experimental/Security/CWE-208/TimingAttackAgainstHash/TimingAttackAgainstHash.ql

‎python/ql/lib/change-notes/2026-01-02-prompt-injection.md‎

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---
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category: minorAnalysis
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---
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* Added experimental query `py/prompt-injection` to detect potential prompt injection vulnerabilities in code using LLMs.
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* Added taint flow model and type model for `agents` and `openai` modules.

‎python/ql/lib/semmle/python/frameworks/agent.model.yml‎

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extensions:
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- addsTo:
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pack: codeql/python-all
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extensible: sinkModel
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data:
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- ['agents', 'Member[Agent].Argument[instructions:]', 'prompt-injection']

‎python/ql/lib/semmle/python/frameworks/openai.model.yml‎

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extensions:
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- addsTo:
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pack: codeql/python-all
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extensible: sinkModel
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data:
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- ['OpenAI', 'Member[beta].Member[assistants].Member[create].Argument[instructions:]', 'prompt-injection']
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- addsTo:
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pack: codeql/python-all
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extensible: typeModel
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data:
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- ['OpenAI', 'openai', 'Member[OpenAI,AsyncOpenAI,AzureOpenAI].ReturnValue']

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

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<!DOCTYPE qhelp PUBLIC
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"-//Semmle//qhelp//EN"
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"qhelp.dtd">
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<qhelp>
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<overview>
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<p>Prompts can be constructed to bypass the original purposes of an agent and lead to sensitive data leak or
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operations that were not intended.</p>
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</overview>
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<recommendation>
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<p>Sanitize user input and also avoid using user input in developer or system level prompts.</p>
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</recommendation>
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<example>
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<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>
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<sample src="examples/example.py" />
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</example>
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<references>
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<li>OpenAI: <a href="https://openai.github.io/openai-guardrails-python">Guardrails</a>.</li>
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</references>
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</qhelp>

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

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

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

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

‎python/ql/src/experimental/semmle/python/Concepts.qll‎

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*/
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DataFlow::Node getABody() { result in [super.getPlainTextBody(), super.getHtmlBody()] }
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}
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/**
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* A data-flow node that prompts an AI model.
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*
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* Extend this class to refine existing API models. If you want to model new APIs,
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* extend `AIPrompt::Range` instead.
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*/
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class AIPrompt extends DataFlow::Node instanceof AIPrompt::Range {
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/** Gets an input that is used as AI prompt. */
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DataFlow::Node getAPrompt() { result = super.getAPrompt() }
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}
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/** Provides a class for modeling new AI prompting mechanisms. */
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module AIPrompt {
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/**
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* A data-flow node that prompts an AI model.
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*
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* Extend this class to model new APIs. If you want to refine existing API models,
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* extend `AIPrompt` instead.
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*/
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abstract class Range extends DataFlow::Node {
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/** Gets an input that is used as AI prompt. */
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abstract DataFlow::Node getAPrompt();
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}
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}

‎python/ql/src/experimental/semmle/python/Frameworks.qll‎

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private import experimental.semmle.python.frameworks.Twisted
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private import experimental.semmle.python.frameworks.JWT
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private import experimental.semmle.python.frameworks.Csv
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private import experimental.semmle.python.frameworks.OpenAI
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private import experimental.semmle.python.libraries.PyJWT
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private import experimental.semmle.python.libraries.Python_JWT
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private import experimental.semmle.python.libraries.Authlib

‎python/ql/src/experimental/semmle/python/frameworks/OpenAI.qll‎

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/**
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* Provides classes modeling security-relevant aspects of the `openAI` Agents SDK package.
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* See https://github.com/openai/openai-agents-python.
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* As well as the regular openai python interface.
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* See https://github.com/openai/openai-python.
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*/
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private import python
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private import semmle.python.ApiGraphs
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/**
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* Provides models for agents SDK (instances of the `agents.Runner` class etc).
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*
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* See https://github.com/openai/openai-agents-python.
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*/
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module AgentSDK {
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/** Gets a reference to the `agents.Runner` class. */
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API::Node classRef() { result = API::moduleImport("agents").getMember("Runner") }
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/** Gets a reference to the `run` members. */
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API::Node runMembers() { result = classRef().getMember(["run", "run_sync", "run_streamed"]) }
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/** Gets a reference to a potential property of `agents.Runner` called input which can refer to a system prompt depending on the role specified. */
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API::Node getContentNode() {
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result = runMembers().getKeywordParameter("input").getASubscript().getSubscript("content")
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or
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result = runMembers().getParameter(_).getASubscript().getSubscript("content")
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}
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}
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/**
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* Provides models for Agent (instances of the `openai.OpenAI` class).
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*
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* See https://github.com/openai/openai-python.
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*/
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module OpenAI {
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/** Gets a reference to the `openai.OpenAI` class. */
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API::Node classRef() {
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result =
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API::moduleImport("openai").getMember(["OpenAI", "AsyncOpenAI", "AzureOpenAI"]).getReturn()
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}
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/** Gets a reference to a potential property of `openai.OpenAI` called instructions which refers to the system prompt. */
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API::Node getContentNode() {
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exists(API::Node content |
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content =
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classRef()
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.getMember("responses")
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.getMember("create")
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.getKeywordParameter(["input", "instructions"])
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or
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content =
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classRef()
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.getMember("responses")
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.getMember("create")
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.getKeywordParameter(["input", "instructions"])
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.getASubscript()
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.getSubscript("content")
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or
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content =
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classRef()
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.getMember("realtime")
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.getMember("connect")
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.getReturn()
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.getMember("conversation")
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.getMember("item")
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.getMember("create")
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.getKeywordParameter("item")
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.getSubscript("content")
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or
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content =
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classRef()
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.getMember("chat")
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.getMember("completions")
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.getMember("create")
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.getKeywordParameter("messages")
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.getASubscript()
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.getSubscript("content")
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|
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// content
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if not exists(content.getASubscript())
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then result = content
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else
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// content.text
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result = content.getASubscript().getSubscript("text")
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)
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}
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}

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