Executive Summary
This application provides an interactive analysis of the Microsoft 365 Copilot attack surface, based on internal research report v1.2. It translates the technical findings into an explorable format, focusing on new threat vectors, detection gaps, and essential governance. The primary takeaway is that Copilot redefines the enterprise attack surface by acting as a powerful “privilege multiplier” that blurs the lines between user identity, data access, and automated actions.
Core Threat Concept
Copilot acts as a privilege multiplier, operating under the full identity-bound context of the user. An attacker with control of a user’s prompt can inherit all their permissions, automating discovery and action at machine speed.
Primary Risk
Blurred boundaries between data, prompts, and actions enable new forms of privilege escalation and data exfiltration. Traditional security telemetry (logs, etc.) currently misses the AI’s “reasoning pipeline,” creating critical detection gaps.
Key Finding: EchoLeak
The “EchoLeak” vulnerability (CVE-2025-32711) demonstrates a critical zero-click Large Language Model (LLM) prompt injection vector within M365, confirming the theoretical risks with empirical evidence.
The AI-Centric Attack Chain
This section details the 7 phases of an attack adapted for an AI-driven environment like M365 Copilot. The techniques shown are specific to how an attacker would leverage Copilot to automate and accelerate their objectives. Click any phase below to see the associated techniques, descriptions, and potential forensic signals or mitigation hints.
Detection & Response Framework
Effective defense requires new telemetry and detection logic. This section outlines the critical detection gaps identified in the research, the recommended SIEM (Security Information and Event Management) rules and logging measures, and the core performance indicators (KPIs) for a successful blue team response.
Target KPIs (Red/Blue Tests)
The following chart outlines the minimum target KPIs for a security operations team to effectively counter these new AI-driven threats.
Detection Gaps ⚠️
- No unified Copilot prompt telemetry or retention controls.
- Inadequate AI context audit trails within Graph API events.
- Limited correlation between LLM-generated actions and user identity logs.
- Inability to baseline benign summarization vs. exfiltration-at-scale.
Detection Recommendations 🛡️
- Enable AI context-layer logging (prompts, embeddings, completions).
- Integrate Copilot logs into SIEM and correlate with Graph/SharePoint data.
- Deploy prompt firewalls and context-boundary tokenization.
- Create SIEM rules for: high-volume prompts + external link creation; prompt reuse across tenants; sudden spikes in summarization bytes-out.
Core Telemetry Schema
A unified logging schema is required to correlate AI activity with traditional security events. The following schema is proposed as a minimum viable standard for detection engineering.
| Field Name | Type | Description |
|---|---|---|
| prompt_hash | sha256 | Hashed prompt text for correlation. |
| prompt_origin | enum | [file, chat, loop, plugin, api] |
| graph_api_call_id | string | Correlates to Graph API audit logs. |
| action_taken | enum | [read, summarize, send, create, update] |
| anomaly_score | float | Behavioral anomaly score (if available). |
Governance & Compliance
Beyond technical controls, robust governance is critical to managing AI risk. This section outlines mandatory policy enhancements and maps the identified risks to major compliance frameworks like the EU AI Act and ISO 23894.
Governance Enhancements ⚖️
- Mandatory Prompt Audit Policy: Define retention windows and hashing policies for prompt privacy and forensic analysis.
- Model Governance Board: Establish oversight aligned with EU AI Act (Art. 9-15) and ISO 23894 operational risk standards.
- Plugin Risk Assessment: Implement marketplace controls, code signing, and vetting for all third-party AI app integrations.
- Forensic Standards: Update evidence collection to preserve prompt hashes, file versions, Graph call IDs, and SIEM logs with chain-of-custody.
Compliance & Ethics 📜
Key risks and their alignment with legal checklists:
- Frameworks: EU AI Act (Art. 9 & 15) ISO 23894
- Identified Risks: Context Leakage Bias Amplification Data Residency Violation
-
Legal Checklist:
- Data Protection Impact Assessment (DPIA) for Copilot use-cases.
- Retention policy aligned with GDPR.
- Responsible disclosure plan for new vulnerabilities.
Research Methodology
This analysis is based on reproducible lab protocols and empirical evidence. This section provides transparency into the research process, including the steps to replicate findings and the known limitations of this investigation.
Lab Protocol 🔬
- Provision isolated M365 tenant(s) with test accounts.
- Populate SharePoint/OneDrive with controlled documents embedding test-prompts.
- Enable Copilot and create controlled plugin consent flows.
- Execute benign and malicious prompt sequences while capturing SIEM, Graph API, and network telemetry.
- Correlate data to create and test detection rules.
Known Unknowns ❓
- [P1 – High] Exact vendor-side Copilot telemetry schema accessible to tenant admins.
- [P2 – High] Scale/prevalence metrics for plugin consent abuse in real tenants.
- [P3 – Medium] Behavioral baseline distinguishing benign summarization from exfiltration-at-scale.
Empirical Evidence 📄
- EchoLeak (CVE-2025-32711): Whitepaper (2025) demonstrating zero-click LLM prompt injection in M365 Copilot.
- Guardz (2025): Attack-surface taxonomy and PoC artifacts referenced for technique names and patterns.
- Lab Replication: Internal sandbox reproduction of EchoLeak PoC (artifact: lab-sandbox-echo-poc-v1.zip).
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