LLM penetration testing

LLM Penetration Testing Workflow

LLM penetration testing looks for practical ways an attacker can manipulate an AI application, not just whether a model can answer a prohibited prompt.

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Where it fits

  • A buyer requests evidence that the AI feature has been tested against prompt injection.
  • A product includes agent tools that can change data or trigger business workflows.
  • A regulated team needs a retestable process before approving AI features.

Operational steps

  • Define scope: models, prompts, tools, retrieval sources, memory, logging, and user roles.
  • Run adversarial tests across direct prompts, indirect content, multi-turn conversations, and tool outputs.
  • Document exploitability, impact, reproduction steps, and server-side control gaps.
  • Retest fixes and keep the test pack in CI so the issue does not return.

Common risks

  • The test stops at model behavior and misses app-level authorization failures.
  • Findings are not mapped to owners or release gates.
  • A fix is made in the prompt but not enforced in backend policy checks.

How PromptGuard Scan fits the workflow

PromptGuard Scan gives penetration testers and product security teams a repeatable harness, useful reports, and checkout-ready plans for ongoing LLM release testing.

Ready to test a real AI surface?

Pricing

Team annual is selected by default.

Annual billing is 50% off. All plans use NOWPayments checkout and keep the product page open.

Dev

For solo builders validating one product before launch.

$25/mo
$294 billed yearly. Save 50%.
5 apps500 scans
  • Prompt injection scans
  • Jailbreak template checks
  • PII and key leak detection
  • HTML risk report
  • Email support

Enterprise

For platform teams, private deployments, and audit-heavy AI systems.

$250/mo
$2,994 billed yearly. Save 50%.
Unlimited appsUnlimited scans
  • Everything in Team
  • Private deployment path
  • Custom test packs
  • Compliance evidence exports
  • Priority security review support

Security playbooks

Practical guides for LLM app security decisions.