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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

    Repository Access Control

    Enforce least-privilege access to codebases. Prevent AI from accessing sensitive repositories or leaking proprietary code.

  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

    Dependency Analysis

    Flag suggestions that include vulnerable or malicious dependencies. Integrate with vulnerability databases for real-time checks.

  6. 06

    Developer Activity Audit

    Complete logging of AI-assisted code generation. Track which suggestions were accepted and identify potential security issues.

Why PromptGuard for code assistants?

  • Purpose-built code security
  • Injection pattern detection
  • Repository access controls
  • Secret detection built-in
  • Developer activity auditing

How it works for code assistants

  1. 01

    Integrate

    Deploy PromptGuard as a gateway for your AI coding tools. Works with Copilot, Cursor, and custom code assistants.

  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(
    api_key="your-api-key",
    project_id="code-assistant"
)

# Configure code-specific protections
pg.configure({
    "code_mode": True,
    "code_security": {
        "detect_injection": True,
        "scan_for_vulnerabilities": True,
        "block_dangerous_patterns": True,
        "allowed_languages": ["python", "javascript", "typescript"]
    },
    "secret_detection": {
        "enabled": True,
        "types": ["api_key", "password", "token", "private_key"],
        "action": "redact"
    },
    "command_validation": {
        "enabled": True,
        "block_dangerous_commands": True,
        "require_approval_for": ["rm", "sudo", "curl", "wget"]
    },
    "repository_access": {
        "enforce_boundaries": True,
        "blocked_paths": [".env", "secrets/", "credentials/"]
    }
})

# AI code suggestions are now protected
response = pg.guard(
    prompt=code_context,
    context={
        "developer_id": developer.id,
        "repository": repo.name,
        "language": "python"
    }
)

The controls this sector runs on

Secure your AI code assistant

Enable AI-powered development without compromising security.

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