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Stealth Layer Security

Service

LLM & AI Security

Authorized AI security assessments of LLM-powered and AI-enabled applications, focused on prompt injection testing, model abuse, sensitive data exposure, and the authentication, authorization, and backend risks introduced by AI features.

What We Test

Coverage areas applied through manual validation and tool-assisted analysis.

  • AI application security assessment across user, system, and tool boundaries
  • Prompt injection (direct and indirect) and jailbreak resistance
  • Model abuse scenarios including misuse, denial of wallet, and unsafe output handling
  • Sensitive data exposure through prompts, embeddings, logs, and training context
  • Insecure plugin, tool, and function-calling integrations
  • Authentication and authorization issues in AI-enabled workflows and agents
  • API and backend risks connected to AI features (SSRF, IDOR, secret exposure)
  • Retrieval-augmented generation (RAG) data poisoning and source trust boundaries
  • Excessive agency, unsafe automation, and over-permissioned AI actions

Methodology

Assessments are aligned with the OWASP Top 10 for LLM Applications and adapted to your specific architecture, including model providers, orchestration layer, tool integrations, and downstream systems the AI can reach.

We combine manual adversarial prompting with targeted testing of the surrounding application, APIs, and trust boundaries — confirming exploitability against live behavior before reporting.

Deliverables

What you receive at the close of the engagement.

  • Executive summary
  • Technical findings with verified reproduction steps
  • Impact, severity, and business risk ratings
  • Evidence including prompts, responses, requests, and screenshots
  • Prioritized remediation guidance for application and model layers
  • Optional retest summary

Who This Service Is For

Organizations and product teams that most commonly engage us for this assessment.

  • AI chatbots
  • AI copilots
  • RAG systems
  • AI customer support
  • Internal AI assistants
  • Enterprise AI applications
  • AI-enabled SaaS
  • Agentic and tool-using AI systems

When Organizations Typically Need This Assessment

Common moments in a product or compliance lifecycle where this assessment adds the most value.

  • Before releasing AI features to customers
  • Before connecting an LLM to sensitive tools or data
  • Before enabling autonomous or agentic behavior
  • After significant changes to prompts, tools, or retrieval sources
  • Before SOC 2 or ISO 27001 readiness that includes AI features
  • Prior to enterprise customer security reviews of AI functionality
  • Following a suspected AI-related security incident
  • Before production deployment of a new AI workflow

Engagement Timeline

A typical engagement moves through the following phases. Exact durations vary with scope and complexity.

  1. Scope Definition

    Model providers, prompts, tools, retrieval sources, and downstream systems are documented.

  2. Authorization

    Written authorization and Rules of Engagement are signed.

  3. Information Gathering

    We map the AI surface: system prompts, tools, RAG sources, and trust boundaries.

  4. Manual Assessment

    Adversarial prompting, tool abuse, retrieval poisoning, and application-layer testing.

  5. Validation

    Findings are reproduced end-to-end, including downstream impact on tools and data.

  6. Reporting

    Executive summary and technical findings with prompts, responses, and evidence.

  7. Debrief

    Walkthrough with product, ML, and security stakeholders.

  8. Optional Retesting

    Confirmation that reported issues have been resolved.

Example Finding

Illustrative only. Not a real client engagement. Provided to convey the level of detail included in reports.

HIGHIllustrative example

Indirect Prompt Injection via Ingested Support Document

Business Impact
An attacker-controlled document ingested by the RAG pipeline could override the assistant's system instructions and cause the AI support agent to disclose internal tooling and issue unauthorized actions on the user's behalf.
Recommendation
Treat retrieved content as untrusted input, constrain tool-calling to strict schemas, enforce per-tool authorization on the server side, and add output-side guardrails and monitoring for anomalous tool invocations.

Frequently Asked Questions

Answers to the questions clients most often ask before engaging us for this assessment.

What is an AI or LLM security assessment?

A structured review of an AI-enabled application covering prompt injection, model abuse, sensitive data exposure, insecure tool integrations, and the authentication and backend risks introduced by AI features.

How long does an LLM security assessment take?

Most engagements run one to three weeks depending on the model architecture, tool integrations, and the number of downstream systems the AI can reach.

What methodology do you follow?

Assessments are aligned with the OWASP Top 10 for LLM Applications and adapted to your architecture, including providers, orchestration, tools, and downstream systems.

Do you test prompt injection?

Yes. We test both direct and indirect prompt injection, jailbreak resistance, and the resulting impact on tools, data, and downstream systems.

Do you test the surrounding application and APIs?

Yes. AI features usually introduce or amplify traditional issues such as SSRF, IDOR, secret exposure, and over-permissioned actions — we test those as part of the engagement.

Will testing affect production models?

We prefer a staging or evaluation environment. When production testing is required, we agree on rate limits and coordinate a window to avoid impacting real users.

Do you provide remediation guidance for both the model and the application layer?

Yes. Recommendations cover prompt design, tool authorization, retrieval trust boundaries, output handling, and application-layer controls.

Can you retest after remediation?

Yes. An optional retest summary confirms that reported issues have been resolved.

Related Resources

Additional context on how we work, what we deliver, and how we protect client data.

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