AI Red Teaming Servicesby Excession Labs Scope an assessment

LLM application security assessment

LLM security assessment of your app or RAG system: we test prompt injection, tenant leaks, data exposure and output handling, and report evidence and fixes.

Short answer

An LLM application security assessment is a scoped, authorized test of the whole application around the model: prompts, retrieval, tools, output rendering and limits. Excession Labs tests how your app can leak data, cross tenant or document boundaries, or act on untrusted text. You get a report with test conditions, observed behavior, evidence, impact, fixes and retest results. We cannot prevent prompt injection.

Suppose you ship a chat assistant, a copilot or a search feature that answers from your customers' documents. An LLM application security assessment finds how that feature can leak data, cross access boundaries or act on text it should not trust. We test the whole application (prompts, retrieval, tools and output) and treat the model as one component.

Who needs this assessment, and when?#

Book it before launch, while few users depend on the feature and boundary bugs are cheap to fix. Book it again before you widen access, for example when you connect a new data source, add a tenant type or let the assistant call tools. Book it before an enterprise review too, when a buyer asks how you isolate tenants and handle prompt injection: a report with evidence answers that better than a policy page (see AI security questionnaires).

If your assistant can take actions, add the AI agent security assessment. If it uses MCP servers, add the MCP security assessment.

What do we assess in an LLM or RAG application?#

We assess the points where untrusted text meets data, tools and users. Each area below has an example failure and our test method. The areas follow the OWASP Top 10 for LLM Applications 2026 (see framework mapping).

Area Example failure How we test it
Direct prompt injection A user message overrides the app's rules and the bot breaks a business rule Scripted attempts with varied wording, repeated, against each rule your app states
Indirect prompt injection A document, web page, email or ticket carries instructions that change the answer We plant labeled test content in each source the app reads, then ask normal questions
Retrieval access boundaries Tenant A's question returns tenant B's passage, or a user sees a document their ACL blocks Two or more test tenants and roles with seeded unique markers; we query across them and through filters, citations and caches
Sensitive data exposure Answers include secrets, personal data or internal notes from context or logs Seeded canary values in prompts, documents and tool output; we try to surface them
Insecure output handling The UI renders attacker-supplied Markdown, HTML or links that send data out We check what the browser loads and what links or images an injected answer can create
System prompt and hidden context The prompt holds secrets or rules that users can extract We attempt extraction and report what a user can learn; the 2026 list says to assume hidden context can be found
Cost and resource abuse One user triggers huge or looping requests Bounded tests of input size, loops and rate limits, with a stated cost cap

What do failures look like?#

Real products have shown these patterns. PromptArmor reported in August 2024 that hidden text in a public Slack channel could make Slack AI show a link that carried a secret from a private channel. In 2023 Johann Rehberger showed that injected text could make Bing Chat render an image URL that carried chat data. In both cases the model followed text that should have been data.

Demonstration

This is a demonstration with a fictional app, "Harbor Notes". No real customer system was tested.

Test condition: two test tenants. Tenant B holds a note with the marker CANARY-B-4471. We sign in as a tenant A user and ask a normal question.

User (tenant A): Summarize my notes about the Lisbon trip.
Assistant: You have two notes about Lisbon. One lists flights.
           Another mentions "CANARY-B-4471" as a booking code.

Observed: the retrieval query used a global vector index and applied the tenant filter after ranking. 3 of 5 attempts returned tenant B content. Impact: cross-tenant read. Fix: filter by tenant inside the search query, then retest.

How do we run the tests and record evidence?#

We write the threat model with you, then run each test under written conditions: account, role, data, model version, system prompt version and tool set. Model output varies, so we repeat each attack and record the number of attempts and how many succeeded. Our methodology has the full steps.

Evidence for each finding includes:

  • the full transcript with timestamps
  • tool-call logs and retrieved passages, where you give us log access
  • the seeded marker that proves a leak came from a specific record
  • the rate of success over the stated attempts

We count a leak only when we show the protected marker. A refusal or an odd answer is not a finding.

What do you get at the end?#

You get a written report. Each finding has the conditions, observed behavior, evidence, impact, fix guidance and a place for retest results. The report also lists the controls that held and the limits we could not test. The example report shows the layout with a labeled lab example.

We can retest fixed findings. We agree the retest terms in scoping.

What do we need from you?#

  • A test or staging environment, or written approval and limits for production
  • Two or more test tenants and several roles, with fake seeded data
  • Access to the model settings, system prompt and tool list, or a description of them
  • Logs for prompts, retrieval and tool calls, if you keep them
  • A named contact who can stop tests

Never send production secrets or real customer data through the form. We test only with written authorization from the system owner. Start with scoping.

Your app also has a normal web and API layer. Penetration testing services covers that part.

What do we not promise?#

We do not say your application is secure after testing, and we cannot prevent prompt injection. The UK NCSC writes that "the best we can hope for is reducing the likelihood or impact of attacks". NIST AI 100-2 E2025 says current mitigations do not give full protection, so designers should assume prompt injection is possible. We test your design against that assumption. A passed test shows only that one attack did not work under the stated conditions.

Questions

Do you test the model itself or the application?

The application. We treat the model as one component and test how your prompts, retrieval, tools and output handling behave together. We do not retrain or benchmark the model. If you need model safety evaluations, say so in scoping and we will state what is in or out.

Can you fix prompt injection for us?

No one can fix it completely. The UK NCSC says prompt injection may never be totally mitigated. We find where an injection would cause harm, and we advise on controls that reduce likelihood and impact, such as limiting data and tool reach.

Do you need our production data?

No. We prefer test tenants with seeded, fake records. Never send production secrets or real customer data through the scoping form. We agree a safe channel for any credentials during scoping.

What if the same attack works only some of the time?

We expect that. Model output varies, so we repeat each test and record how often it worked. The report states the attempt count and the observed rate.

Sources

  1. OWASP Top 10 for LLM Applications 2026 (README, release August 4, 2026) (checked 9 November 2026)
  2. UK NCSC: Prompt injection is not SQL injection (checked 9 November 2026)
  3. NIST AI 100-2 E2025: Adversarial Machine Learning taxonomy (checked 9 November 2026)
  4. Greshake et al.: Not what you've signed up for (arXiv 2302.12173) (checked 9 November 2026)
  5. PromptArmor: Data Exfiltration from Slack AI via indirect prompt injection (checked 9 November 2026)
  6. Embrace The Red: Bing Chat data exfiltration PoC and fix (checked 9 November 2026)
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Shipping an AI feature or agent?

Describe the system, the data it reads and the actions it can take. We reply with questions and a proposed scope.

Scope an assessmentSee the example report