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AI/LMS Security Assessment Case Study

This is a public-safe case study from an authorized assessment of an AI assistant embedded in a learning-management environment.

The private deliverable was a 24-page confidential report produced in May 2026. It documented 16 validated findings from a standard-user session and translated the evidence into remediation guidance. The confidential report stays private. This repository keeps the reusable parts: scope, control questions, finding categories, remediation patterns, narrative lessons, and redaction discipline.

Assessment summary

What This Shows

  • How I scoped an AI/LMS assessment
  • Which control areas I tested
  • How findings were translated into remediation
  • How portfolio evidence can be shared without exposing private systems
  • How to show meaningful assessment work while respecting disclosure boundaries

The Short Version

The interesting part was not one dramatic bug. It was the combination: an AI assistant with tools, memory, document retrieval, LMS context, user-editable behavior, and permissive defaults.

Individually, each issue was fixable. Together, they created a path where a normal user could influence assistant behavior, expose internal details, and potentially move AI-visible data outside the trusted environment.

That is the real lesson: AI security is not just prompt filtering. It is tool permissions, default settings, data boundaries, auditability, memory isolation, document ingestion, and the boring-but-vital question:

What can this assistant touch when a normal user gets clever?

Assessment Areas

Area Review Question
Tool access Can the assistant call external services, platform APIs, or messaging tools beyond the user's expectation?
Instruction hierarchy Can user-editable instructions weaken system or platform controls?
Safety configuration Are guardrails admin-owned, default-on, and hard to bypass from a normal session?
LMS context What user, role, course, and document data enters the AI session automatically?
Retrieval Are knowledge sources scoped by owner, course, role, and document sensitivity?
Memory Can prior session content or uploaded material cross boundaries?
Messaging Can the assistant send trusted communications without review?

Sanitized Findings

Area Risk Pattern Primary Fix
External tools Outbound requests could include sensitive session context Restrict destinations, methods, and data classes; require user approval
Instruction control User-authored instructions could steer behavior beyond intended scope Keep user instructions below platform and system controls
Safety settings Protective controls were exposed or weakly enforced Make safety settings admin-owned and regression tested
Self-disclosure Assistant responses could reveal internal behavior and attack paths Reduce unnecessary introspection and test for disclosure patterns
LMS integration Platform context was broader than needed for the task Minimize injected context and enforce role-aware scopes
Shared knowledge Retrieval boundaries could expose inappropriate documents Add document-level ownership and course-section scoping
Messaging tools Trusted-session messages could be abused Require review before sending from a platform identity

Recommended Controls

  • Allowlist external tools by domain, method, and approved data type.
  • Require visible user approval when requests include session or LMS context.
  • Log tool calls with actor, destination, method, time, and sanitized metadata.
  • Keep user-authored instructions below system and platform instructions.
  • Make safety controls admin-owned, default-on, and covered by regression tests.
  • Scope LMS retrieval by owner, course, role, section, and document sensitivity.
  • Review generated messages before sending from a trusted identity.

Private Report Structure

The private report followed a formal assessment format:

  • executive summary and risk summary
  • scope, authorization context, and methodology
  • target reconnaissance from a standard-user session
  • detailed findings with severity, evidence, impact, and remediation guidance
  • attack-chain narrative showing how multiple control weaknesses could combine
  • screenshot evidence index for private remediation teams
  • assessor declaration and disclosure boundary

The public repository does not include exploit strings, screenshots, target URLs, student data, internal hostnames, API paths, tokens, headers, or reproduction steps.

Finding Themes

ID Theme Severity
F-01 Unrestricted outbound requests from AI tools Critical
F-02 User-editable high-priority instructions Critical
F-03 Safety/context bypass exposed in assistant settings Critical
F-04 Assistant disclosed its own attack surface Critical
F-05 LMS API calls attempted from assistant tooling High
F-06 Cloud command proxy behavior indicated by tool errors High
F-07 Broad tool access enabled by default High
F-08 External response content influenced chat context High
F-09 User context auto-injected into AI sessions Medium
F-10 Persistent memory checked automatically Medium
F-11 Uploaded and fetched documents entered AI context Medium
F-12 Lab-style framing produced risky command guidance Medium
F-13 Platform and architecture details disclosed Informational
F-14 Internal prompt/configuration details exposed Critical
F-15 Email tool presented phishing/impersonation risk High
F-16 Personal documents appeared in shared knowledge base High

Public Boundary

Published:

  • Assessment workflow
  • Control matrix
  • Redacted narrative report
  • Remediation playbook
  • Redaction standard
  • Sanitized report template
  • LinkedIn-ready project copy

Withheld:

  • Confidential report and evidence
  • Target URLs, tenant IDs, course IDs, and organization names
  • Exploit prompts, payloads, screenshots, tokens, headers, and internal endpoints
  • Student, staff, document, message, or academic-record data

Documents

The underlying assessment was authorized and reported privately. This version is for portfolio review and does not provide reproduction steps.

About

Authorized AI/LMS security assessment case study with private reporting, OWASP LLM-aligned controls, remediation guidance, and public-safe redaction boundaries.

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