Manual testing. Human-verified findings.

AI and LLM penetration testing

AI penetration testing assesses the security of an LLM application or agent, including its data access, tool permissions, and human approval boundaries. Balhence evaluates the feature in your authorized environment, maps relevant findings to OWASP LLM and agentic risks, and confirms each reported issue through human review.

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What we test in an AI feature

What can it read?

Review access to context, retrieval-augmented generation (RAG) sources, memory, and customer data. The scope includes how permissions follow information into a model workflow.

What can it do?

Assess the tools and functions available to the application and whether authorization remains tied to the user and approved task.

When does a person approve?

Review approval steps for consequential actions, including how approval applies when an action or its inputs change.

Testing against the OWASP LLM Top 10 (2026)

The OWASP GenAI LLM Top 10 2026 informs the coverage plan. We agree which risks and interfaces apply to your feature, then record what was assessed and any limits.

  • Prompt injection: instruction and data boundaries across user input, retrieved content, and supported media.
  • Sensitive information disclosure: access to private context and customer information.
  • Excessive agency: tool scope, privileges, and approval requirements.
  • Supply chain and data/model poisoning risks relevant to your dependencies and update process.
  • Hidden context exposure and improper output handling, evaluated for their actual security impact.

Framework coverage is a planning aid, not a certification or a guarantee that every model behavior has been tested.

AI agent and agentic workflow security testing

Agents add tool calls, persistent memory, and delegation. We examine the permissions and approval boundaries across those components, using the OWASP Top 10 for Agentic Applications as a reference. The plan records approved identities, integrations, and what actions are allowed. An anonymized workflow field note illustrates why the server must enforce required checks before advancing a state; the same authorization principle applies to agent tool actions.

If agent tools access cloud resources, identify the workload identities, permitted resources and approved actions. Coordinate cloud IAM and workload testing when that additional depth is needed; AI workflow testing alone does not establish coverage of the whole cloud account.

Where useful, findings can be discussed in the context of MITRE ATLAS and the NIST AI Risk Management Framework. These references do not imply certification.

Prepare an LLM application testing scope

Start with a diagram of the model, retrieval sources, memory, tools, and approval steps. For each user role, describe what information it may access and which actions it may request.

Record the model and application versions, supported input types, approved integrations, and configuration in use. Provide controlled accounts with different permission levels and test records that can be distinguished from real customer information.

Agree request and cost limits, allowed tool actions, logging, and stop conditions. After changes to the model, retrieval rules, or permissions, review which earlier checks remain relevant and what needs testing again. Use these details to discuss your LLM testing scope.

How we test AI safely

Use controlled accounts, approved tools, and an environment you authorize. Agree rate and cost limits, test data, permitted actions, logging, and stop conditions. Consequential actions need explicit scope and human control.

No model autonomously tests production. Credentials, source code, and unredacted findings go to an AI provider only when you explicitly approve the provider and terms. Read how we use AI and our LLM application testing guide.

AI and LLM pentest reports and remediation evidence

  • An executive summary with the tested feature, model and configuration versions where available, dates, methodology, and coverage limits.
  • Human-verified findings with impact, prerequisites, evidence, severity reasoning, and practical fixes.
  • A report walkthrough and an agreed route for urgent findings.
  • A bounded re-test of agreed fixes when included in the agreement, with each finding recorded as fixed, partially fixed, not fixed, or not re-tested.
  • The evaluated data sources, retrieval boundaries, tool permissions, and approval rules, with unavailable or excluded integrations documented.

Findings include relevant OWASP mappings and practical recommendations for permissions, approval design, data access, and output handling.

AI and LLM penetration testing FAQ

Is this the same as red-teaming a base model?

The focus is your deployed application: how it connects the model, data, tools, permissions, and people. Base-model behavior research and wider safety evaluations need a separately agreed scope.

Can you assess an application using a third-party model?

Yes, subject to the provider terms and your authorization. We assess your application and approved integrations; ownership of an application does not authorize testing unrelated provider infrastructure.

Does AI-native mean automated-only testing?

No. Approved AI tools can assist analysis, but a person designs the assessment, validates findings, and owns delivery.

Remote engagements for teams in India and US companies include agreed time-zone overlap, access and evidence-handling terms.

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Scope your AI or LLM application security test

Tell us about the systems, roles, integrations, and deadline. We will confirm scope, availability, written authorization, and deliverables before testing.

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