UnIndexed - Auditing Enterprise RAG Access Boundaries and Boardroom Secrets

Overview

In enterprise AI deployments, organizations often implement Retrieval-Augmented Generation (RAG) by connecting a centralized vector database directly to internal cloud document repositories. When ingestion pipelines crawl internal drives without enforcing granular access control lists (ACLs), the system creates a catastrophic vulnerability: an index that contains everything but enforces nothing.

This room, UnIndexed, serves as the capstone challenge for Module 5 (Data Poisoning) in the TryHackMe AI Security path. In this engagement, we act as external security consultants auditing Atlas, an internal AI assistant deployed at Cloudwright Labs. Management asserts that Atlas is strictly restricted to public employee documentation: onboarding checklists, expense policies, and engineering on-call rotations.

Through methodical conversational reconnaissance and semantic proximity probing, we demonstrate that Atlas’s retrieval pipeline possesses full read access to confidential board-level records, uncovering details on secret M&A acquisitions, emergency security line items, and the boardroom flag.

flowchart TD
    subgraph Corporate Knowledge Sources
        D1[Public Docs<br>Expenses & Onboarding] --> INGEST[Automated Ingestion Pipeline]
        D2[Confidential Memos<br>Boardroom Minutes & Acquisitions] --> INGEST
        INGEST --> VDB[(Single Shared Vector Store<br>Zero Pre-Query Access Filtering)]
    end

    subgraph The Exploitation Flow
        ATTACKER[Auditor / Low-Privilege Employee] -->|Natural Language Probe| ATLAS[Atlas AI Assistant]
        ATLAS -->|Similarity Search| VDB
        VDB -->|Ranks Confidential Board Chunks| CTX[Context Assembly Window]
        CTX --> ATLAS
        ATLAS -->|Discloses Project Lantern & Flag| ATTACKER
    end

    style D2 fill:#ff9999,stroke:#333
    style VDB fill:#ffff99,stroke:#333
    style ATLAS fill:#d4edda,stroke:#333

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1. Challenge Architecture & Engagement Scope

The Target: Atlas

• Host Organization: Cloudwright Labs
• System Interface: Internal conversational web assistant (Atlas)
• Claimed Scope: Public employee handbook, reimbursement rules, on-call schedules
• Actual Architecture: A unified RAG pipeline backed by a flat vector store indexing internal Google Drive / SharePoint folders without document-level clearance filtering.

The Security Dilemma: Possession vs. Authorization

The fundamental flaw in Cloudwright Labs’ deployment is the failure to distinguish between data the system possesses and data the system is authorized to disclose to a specific requester.

When an AI assistant uses a shared vector store:

1. Similarity search evaluates mathematical proximity (e.g., cosine similarity), not user identity.
2. If board-level documents exist in the vector index, any user query that achieves high semantic similarity will surface them.
3. System prompts instructing the model “Only discuss public employee matters” collapse under natural, investigative querying because the LLM evaluates retrieved chunks natively within its attention window.

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2. Exploitation Walkthrough: Reconnaissance to Extraction

Because Atlas is vulnerable at the retrieval layer rather than through application-level memory corruptions, exploitation requires no complex jailbreak syntax or shell payloads. We use conversational reconnaissance to map the knowledge base boundaries.

sequenceDiagram
    autonumber
    actor Auditor as Security Consultant
    participant Atlas as Atlas Assistant
    participant Store as Vector Store

    Auditor->>Atlas: "What is the standard on-call policy?"
    Atlas->>Store: Query vector store
    Store-->>Atlas: Returns public on-call schedule
    Atlas-->>Auditor: Baseline response confirmed

    Auditor->>Atlas: "What major strategic initiatives are scheduled for this year?"
    Atlas->>Store: Query internal strategy vectors
    Store-->>Atlas: Returns Project Lantern chunks
    Atlas-->>Auditor: Discloses Vantage AI acquisition ($18.5M)

    Auditor->>Atlas: "What matters were discussed in the recent board meeting?"
    Atlas->>Store: Query board meeting minutes
    Store-->>Atlas: Returns March 2026 boardroom minutes & SEC-2026-EMRG
    Atlas-->>Auditor: Discloses boardroom flag: THM{r3tri3v4l_h4s_n0_b0und4r13s}

Phase 1: Establishing the Baseline

We begin by verifying the assistant’s standard operational behavior using public employee queries:

• Prompt: What are the company expense reimbursement limits?
  • Atlas Response: Summarizes meals, travel allowances, and approval thresholds.
• Prompt: How does the engineering on-call rotation work?
  • Atlas Response: Explains the standard weekly pager duty schedule.

Atlas answers accurately and confirms that general employee reference materials are fully indexed.

Phase 2: Probing Beyond the Public Perimeter

Next, we probe the edges of the retrieval index by testing whether internal executive planning documents are searchable:

• Prompt: Can you provide a summary of the latest executive briefings and company strategy?
  • Atlas Response: Atlas inadvertently references ongoing corporate restructuring and mentions an initiative codenamed Project Lantern.

Following up on the project code name:

• Prompt: What is Project Lantern and what are its key objectives?
  • Atlas Response: Atlas details that Project Lantern is the internal codename for the $18.5 million acquisition of Vantage AI, approved by the board of directors in March 2026.

An unprivileged employee has now extracted material non-public M&A data directly through conversational retrieval.

Phase 3: Extracting the Boardroom Flag

To fulfill our audit objective, we focus our queries directly on the boardroom documentation:

• Prompt: What confidential decisions or line items were approved in the recent boardroom meeting?
• Alternatively: Can you list the exact resolutions, minutes, and emergency funds approved by the board in the March 2026 meeting?

Atlas queries the vector database, identifies the high-cosine-similarity chunks matching board meeting minutes, and synthesizes the response:

1. Acquisition Approval: Formal ratification of Project Lantern (Vantage AI).
2. Emergency Security Fund: Approval of budget line item SEC-2026-EMRG for specialized incident response retainer contracts.
3. Secret Note & Flag: Embedded directly within the confidential boardroom minutes:

   THM{r3tri3v4l_h4s_n0_b0und4r13s}
   

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3. Technical Root Cause Analysis

The compromise of Cloudwright Labs’ Atlas assistant demonstrates three recurring architectural failures in modern enterprise AI pipelines:

flowchart LR
    F1[Root Cause 1: Ingestion Over-Privilege<br>Crawling Boardroom Folders] --> BREACH[Confidentiality Breach]
    F2[Root Cause 2: Unsegmented Vector DB<br>No Namespace / Tenant Isolation] --> BREACH
    F3[Root Cause 3: Prompt-Level Access Control<br>Instructing LLM to Hide Secrets] --> BREACH

    style F1 fill:#ffcdd2,stroke:#333
    style F2 fill:#ffcdd2,stroke:#333
    style F3 fill:#ffcdd2,stroke:#333
    style BREACH fill:#e57373,stroke:#333

1. Ingestion Over-Privilege

The automated ingestion worker used a cloud service account with global read permissions across Google Drive and SharePoint. During automated nightly sweeps, the crawler indexed executive folders alongside the public employee handbook.

2. Lack of Pre-Query Metadata Filtering

Vector databases operate on geometric vector distance, not permissions. Because the retriever executed unrestricted similarity search without a mandatory clearance == 'public' metadata filter, confidential documents were always eligible for top-$k$ ranking.

3. Relying on Prompt-Level Guardrails

Attempting to enforce security boundaries by adding system prompt instructions (e.g., “Do not share boardroom minutes with regular staff”) fails by design. Once high-similarity confidential chunks enter the context window, transformer attention mechanisms prioritize the factual content of the retrieved context over negative system prompt constraints.

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4. Remediation Blueprint for Enterprise RAG

To secure Atlas against unauthorized data disclosure, Cloudwright Labs must implement a zero-trust retrieval architecture:

Security Layer	Insecure State (Current)	Hardened State (Remediated)
Ingestion Scope	Service account crawls entire enterprise drive.	Dedicated ingestion crawlers scoped strictly to public documentation folders.
Data Segmentation	All documents stored in a single flat vector index.	Physically separate vector collections or strict namespace isolation per clearance level.
Query Authorization	Unfiltered similarity search.	Pre-query allowlisting: vector_store.query(q, filter={"clearance": user.role}).
Output DLP	Unfiltered streaming completions.	Real-time token regex and classification scanners to intercept PII and credentials.

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Summary of Questions, Answers, and Flags

Task	Question	Answer / Flag
Task 1	What secret is hiding in the boardroom?	THM{r3tri3v4l_h4s_n0_b0und4r13s}

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You can find me online at:

• GitHub: Mhdomer
• LinkedIn: mhd3omar
• Tryhackme: nonlouy