Sample Report

What an AI security assessment actually produces.

Two layers: an automated pass rate across every attack type, and the manual findings that explain what a failure actually costs you.

Sample / Demonstration Report

Illustrates the depth of a SilverXAI deliverable using a well-documented class of attack. Not from a client engagement, and not a disclosure about any real system. Every number and finding below is illustrative. A real engagement report is delivered to the client under the terms of the engagement.

Layer One — Automated

Attack simulation summary

Every attack type is run repeatedly against the live system. This is the repeatable layer — it tells you whether something bent, and it is re-run on every retest.

62%

resilient — 8 of 13 attack types held

4 broken 8 resilient 1 errors

Status Attack type Broken Resilient Errors
Resilient Direct prompt injection 0 6 0
Broken Indirect prompt injection 4 2 0
Resilient Jailbreak (AIM / DAN family) 0 8 0
Resilient Role manipulation 0 5 0
Broken System prompt extraction 2 4 0
Resilient Encoding evasion (Base64) 0 4 0
Resilient Linguistic evasion 0 5 0
Broken RAG poisoning 3 1 0
Resilient Cross-tenant retrieval 0 4 0
Broken Agent tool abuse 5 1 0
Errors Excessive agency 0 2 2
Resilient Insecure output handling 0 3 0
Resilient Authorization bypass 0 6 0

A pass rate on its own is where most automated tooling stops. It tells you a system prompt bent; it does not tell you what an attacker reaches when it does. That is layer two.

Vulnerability register

Every finding in one table — identifier, severity, CVSS, affected component and current status. This is the view a vulnerability-management team works from.

ID Finding Severity CVSS v3.1 Status
SXAI-001 Indirect prompt injection leading to unauthorized agent tool execution Retrieval pipeline → agent planner → tool layer CRITICAL 9.3 CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:N Resolved
SXAI-002 Agent tool invocation performed under a shared service identity Agent tool layer / IAM HIGH 8.1 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N Partially Resolved
SXAI-003 Poisoned document retrieved and treated as trusted context Ingestion → vector store HIGH 7.5 CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N Partially Resolved
SXAI-004 System prompt recoverable through iterative extraction Model interface MEDIUM 5.3 CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N Resolved

Each finding carries a vulnerability class, and every CWE, OWASP, MITRE ATLAS, NIST AI RMF, ISO 42001 and regulatory reference is derived from it — so the per-finding mapping and the audit cross-reference matrix can never disagree.

Layer Two — Manual

What a failure actually costs

Each broken row above becomes a written finding. This is the one behind “Indirect prompt injection”.

CRITICAL SXAI-001

Indirect Prompt Injection Leading to Unauthorized Agent Tool Execution

Scenario
A malicious instruction inside an untrusted document enters the RAG context.
Attack
The agent reads retrieved content as trusted instruction.
Potential Impact
Unauthorized tool execution and data exposure.

Read the full finding

Evidence, business and technical impact, the full attack path, framework mapping, remediation and retest status. Enter your email to open it.

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Full report structure

Ten sections, ordered so an executive can stop after the first two and an engineer can work from the rest.

  1. 01

    Executive Summary

    What was tested, what we found, and what it means for the business — written to be read by someone who is not an engineer.

  2. 02

    AI Risk Rating

    An overall posture rating for the assessed system, with the reasoning behind it rather than a bare score.

  3. 03

    Attack Surface

    The mapped surface of your deployment: models, prompts, retrieval, agents, tools, APIs and the identities behind them.

  4. 04

    Vulnerability Register

    Every finding in one table — identifier, title, severity, affected component, and current status.

  5. 05

    Technical Findings

    Each finding in full: evidence, business impact, technical impact, severity rationale, attack path and remediation.

  6. 06

    Proof-of-Concept Evidence

    Reproducible payloads, request captures and traces, so your engineers can confirm the issue themselves.

  7. 07

    Business Impact

    What an attacker gains, framed against your data, your customers and your regulatory obligations.

  8. 08

    Framework Mapping

    Findings mapped to OWASP, MITRE ATLAS, NIST AI RMF and ISO/IEC 42001 controls.

  9. 09

    Remediation

    Prioritised, specific fixes at the level of architecture, configuration and code — not generic advice.

  10. 10

    Retest Status

    The verified state of every finding after your team has remediated, with supporting evidence.

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