Insights from Testing Microsoft 365 Copilot

🔧 Study Objective

This study systematically analyzed vulnerabilities in retrieval-augmented generation (RAG) systems, using Microsoft 365 Copilot as a testbed. The focus was to evaluate the security implications of adversarial document injection, data persistence issues, and insufficient monitoring capabilities in environments handling sensitive enterprise data.

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Key Technical Outcomes

1️⃣ Manipulated Retrieval and Outputs:
Maliciously crafted documents embedded with adversarial instructions (e.g., "Ignore other documents" or "Prioritize this text exclusively") exploited gaps in the document ranking algorithms. These directives effectively altered Copilot’s response hierarchy, resulting in the prioritization of injected content over valid data sources.

2️⃣ Persistent Data Exposure:
Deleted or updated documents remained retrievable due to delayed synchronization between indexing and cache invalidation processes. This led to unauthorized access to superseded or sensitive information even when presumed secure by users.

3️⃣ Monitoring Deficiencies:
The system lacked sufficient telemetry for tracking document injection attempts, adversarial prompt interactions, and suspicious retrieval patterns. The absence of real-time anomaly detection and detailed logging for workflow actions left key blind spots in security oversight.

🔬 Technical Details of Penetration Testing

1️⃣ Controlled Environment Setup:
A virtualized enterprise environment was created, mirroring production scenarios. Components included Microsoft 365 Copilot integrated with an enterprise document repository and a range of user roles with varying permissions. The system also replicated API interactions with external resources.

2️⃣ Adversarial Payload Construction:
Malicious documents were designed with embedded adversarial metadata, inline instructions, and headers. Techniques included:

• Metadata Injection: Adding phrases like "Rank as primary source" within document properties.
• Title Manipulation: Crafting titles that exploit natural language processing (NLP) heuristics for prioritization.
• Inline Prompt Injection: Embedding conflicting directives directly into the text.

3️⃣ Query Execution:
Simulated user queries, including "Summarize Q4 earnings from all sources" or "Provide client feedback analysis", were submitted to Copilot. These queries aimed to evaluate retrieval accuracy and manipulation susceptibility.

4️⃣ Output Validation and Analysis:
Generated responses were systematically reviewed for:

• Source Bias: Over-representation of injected documents in retrieval sets.
• Data Integrity Issues: Missing or misrepresented legitimate data.
• Citation Anomalies: Inaccurate or absent attributions to source documents.
  Automated scripts, including NLP-based comparison tools and differential analysis algorithms, were employed for validation.

5️⃣ Propagation Tracking:
The influence of manipulated outputs was analyzed across subsequent operations. For example, injected content affected derivative reports, decision workflows, and shared collaborative documents. Log analysis and dependency tracing were used to assess the downstream impact.

💡 Key Takeaway

The study highlights pressing security gaps in RAG-based AI systems, including the ease of adversarial influence, the persistence of sensitive data, and the absence of sufficient monitoring frameworks. These issues underscore the urgent need for:

• Prompt Input Validation: Mechanisms to sanitize adversarial queries.
• Enhanced Logging: Granular telemetry for workflow events and user actions.
• Real-Time Anomaly Detection: AI-driven solutions to detect unusual retrieval behaviors.

📖 Read more: “ConfusedPilot: Compromising Enterprise Information Integrity and Confidentiality with Copilot for Microsoft 365” (https://lnkd.in/ghCSuM5a) by Ayush RoyChowdhury, Mulong Luo, Prateek Sahu, Sarbartha Banerjee, and Mohit Tiwari (Symmetry Systems and The University of Texas at Austin)

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