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USE CASE: AI CODE ASSISTANTS

Secure AI-powered code generation

Prevent code injection, block malicious suggestions, and ensure AI coding tools can't access sensitive repos or execute harmful commands.

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WHAT LEAVES THE EDITOR, AND WHAT COMES BACKschematic
redacted before it leaves the editoryour idesecret scanentropy · sk- AKIA ghp_modelcompletiongenerated-code scan50+ cwe classes · opt-ininsecure completion flagged or blocked
Two legs, two different problems. What leaves the editor carries credentials; what comes back carries code. Entropy and a known-prefix table on the way out, a static analysis of what the model actually wrote on the way back.

Where a coding assistant is exposed

  1. 01

    Code Injection Prevention

    Detect and block attempts to inject malicious code through AI suggestions. Analyze generated code for security vulnerabilities before it reaches developers.

  2. 02

    File Access Checks

    Validate the file reads and writes your coding agent makes before they run. Reads of sensitive system files are refused.

  3. 03

    Command Execution Validation

    Validate shell commands and scripts before execution. Block dangerous operations like rm -rf, privilege escalation, or network exfiltration.

  4. 04

    Secret Detection

    Automatically detect and redact API keys, tokens, passwords, and other secrets in code before they're exposed to AI systems.

  5. 05

    Code Vulnerability Detection

    Flag generated code that carries known-vulnerable patterns, such as injection-prone queries, before a developer accepts it.

  6. 06

    Developer Activity Audit

    Every scan is recorded with its decision and threat type, so you can see what was blocked and why.

Why PromptGuard for code assistants?

  • Code vulnerability detection
  • Injection pattern detection
  • Agent file and shell checks
  • Secret detection built-in
  • Developer activity auditing

How it works for code assistants

  1. 01

    Integrate

    Route your code assistant's LLM calls through PromptGuard, or call the Guard and agent APIs from it.

  2. 02

    Analyze

    Every code suggestion is analyzed for security issues. Malicious patterns are blocked before reaching developers.

  3. 03

    Audit

    Track all AI-assisted code changes. Identify security issues and demonstrate compliance.

Securing AI code assistants

python
from promptguard import PromptGuard

pg = PromptGuard()  # reads PROMPTGUARD_API_KEY

# Check what your coding agent is about to run, before it runs
check = pg.agent.validate_tool(
    agent_id="code-assistant",
    tool_name="run_shell",
    arguments={"command": proposed_command},
)
if not check["allowed"]:
    raise PermissionError(check["reason"])

# Scan code before it leaves for the model: secrets are redacted or blocked
# according to the project's settings
result = pg.security.scan(code_context)
if result.get("blocked"):
    raise PermissionError(result.get("threat_type"))

The controls this sector runs on

Secure your AI code assistant

Enable AI-powered development without compromising security.

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