Generative AI Security: The Complete Guide to GenAI Security
Generative AI is reshaping cybersecurity by enhancing threat detection, simulating attacks, and automating responses—making defenses faster and more adaptive.
Fergal Glynn
Launched in 2023, Microsoft Copilot has 33 million active users. While platforms like ChatGPT are more common among everyday users, Copilot is popular with both businesses and enterprises. Microsoft offers numerous built-in protections for Copilot, but even then, this large language model (LLM) isn’t immune to adversarial attacks like prompt injection.
Prompt injection is one of the most common and damaging threats to Copilot. This guide explains how direct and indirect prompt injections exploit Copilot and how organizations can prevent these attacks.
Traditional ChatGPT deployments often operate closer to a user-model loop, although newer features like tools, plugins, and browsing expand the attack surface beyond text.
Copilot goes further by embedding the model directly within enterprise systems, including Microsoft 365, Teams, Outlook, SharePoint, OneDrive, GitHub, and external plugins. Each connection expands the attack surface.
Every system Copilot can read from or act on becomes part of the context assembly pipeline. Instructions can be transmitted via email, documents, repositories, and APIs before reaching the model. That creates more entry points and more opportunities for hidden instructions to make an impact.
Each point of integration also creates a trust boundary. Copilot has to decide:
Attackers will focus on breaking these control points. Malicious content hidden in emails, documents, issue trackers, or API calls can contain instructions that reach Copilot without looking suspicious.
System instructions themselves can become an attack surface. Mindgard technology has shown that attackers can extract and manipulate hidden system prompts, revealing how fragile instruction hierarchies can be when exposed to adversarial techniques.
Copilot can also trigger real business operations. It can search internal files, summarize sensitive conversations, modify code, and interact with enterprise tools. A successful prompt injection can influence decisions, expose confidential data, or alter workflows across systems.
Prompt injection in Copilot becomes a cross-system problem. This is why organizations need visibility into how AI systems are connected, what data they touch, and where hidden attack paths exist.
Mindgard’s AI Security Risk Discovery & Assessment helps map Copilot’s effective attack surface across models, tools, data sources, and integrations, enabling teams to identify prompt injection risks before they’re exploited.
To operationalize that visibility, organizations need centralized controls across the AI stack. Mindgard’s Offensive Security solution provides unified governance, monitoring, and policy enforcement across Copilot models, data pipelines, tools, and workflows, helping teams manage prompt injection risk at scale rather than in isolated components.
Prompt injection attacks against Copilot generally fall into two categories: direct attacks that appear in the user’s chat input, and indirect attacks that attackers hide within external content.
Direct prompt injections are easier to detect because the malicious instructions are embedded within the chat interface itself.
For example, an attacker may use an instruction-override exploit to instruct Copilot to ignore or override its system instructions. A user might say something like “ignore all previous instructions and do X instead.”
Another common type of direct prompt injection is task manipulation. With this exploit, an attacker can reframe the request, causing Copilot to prioritize malicious goals, such as summarizing sensitive content in an unsafe way.
Indirect prompt injections are more insidious because the attacker never interacts with Copilot directly. Instead, they embed malicious instructions into content that Copilot accesses, including PDFs, emails, external URLs, and code.
Because these artifacts often appear legitimate, organizations need automated ways to inspect AI-consumed content before it reaches Copilot. Mindgard’s AI Artifact Scanning analyzes documents, code, and external data for hidden instructions and adversarial patterns, helping teams detect indirect prompt injection risks before they propagate through enterprise workflows.
Copilot had a previous vulnerability, nicknamed EchoLink, that allowed attackers to send hidden instructions via email. When Copilot summarized or responded to the email, it unknowingly followed those hidden commands, sometimes resulting in data exfiltration.
Copilot doesn’t get attacked through a single interface. Prompt injection often enters through the systems Copilot touches every day.
These paths appear harmless because they blend into normal workflows, making them hard to detect and easy to exploit. More importantly, they create real-world business risks. Here are some common examples.
Hidden instructions are embedded in emails that Copilot summarizes or replies to. Copilot follows the attacker’s instructions while handling legitimate messages, exposing sensitive context.
Business impacts:
Malicious instructions are hidden within internal documents that Copilot retrieves during search or Q&A. Copilot treats the injected content as trusted knowledge, leaking internal data or altering outputs.
Business impacts:
Injected prompts appear as normal collaboration messages in channels or chats. Copilot interprets the attacker’s content as team intent and shifts behavior across workflows.
Business impacts:
Instructions embedded in code comments influence how Copilot generates or modifies code. Copilot produces insecure logic or inserts hidden behavior into production code.
Research into coding agents has shown how malicious instructions embedded in development workflows can manipulate AI behavior and introduce insecure logic into generated code. For example, Mindgard’s technology identified vulnerabilities in the Cline coding agent that exposed how easily agentic tools can be steered by adversarial inputs.
Business impacts:
Third-party tools return content containing hidden instructions via APIs. Copilot executes actions based on untrusted output and crosses system boundaries.
This risk extends beyond traditional APIs. Research into agentic AI systems has shown that attackers can manipulate tool and browser interactions, effectively steering AI-driven workflows across environments.
For example, Mindgard’s technology demonstrated how the Manus Rubra framework enables full browser-level control, illustrating how compromised tools can transform prompt injection into direct operational impact.
Business impacts:
The table below breaks down common prompt injection attack paths in Copilot, their business impacts, and mitigation controls.
The best way to approach LLM security is to assume that a single AI guardrail will fail. Untrusted inputs will test Copilot, so your organization needs layered controls. Follow these best practices to prevent prompt injections in Copilot.
Well-designed system prompts reduce the likelihood that Copilot will follow malicious instructions. Clear role definitions and instruction hierarchies also make it harder for injected prompts to override intended behavior.
You don’t have to do this alone, either. Microsoft also uses techniques such as spotlighting, which helps Copilot distinguish user instructions from external content that may contain hidden or malicious commands.
Copilot is only as safe as the content it consumes. Limit the data you feed into Copilot, and always verify its authenticity.
For example, you should prioritize internal knowledge bases, document libraries, and sanctioned applications over open-ended or user-supplied content.
Malicious actors are continually refining their attack techniques, and changing models and integrations always introduce risk. Continuous testing and red teaming are critical for surfacing vulnerabilities before attackers do. But manual testing can’t keep pace with evolving prompt injection techniques.
Mindgard’s Automated AI Red Teaming continuously simulates real-world adversarial attacks against Copilot, exposing weaknesses in prompts, tools, and workflows before they reach production.
Microsoft has numerous protections and controls built into its ecosystem that help secure Copilot. For example, Microsoft Defender can help by enforcing your security policies across all connected services.
In addition, Prompt Shields in Azure AI Foundry and Azure AI Content Safety help detect and block known or high-risk prompt injection patterns before they affect Copilot’s behavior.
However, real-world research has shown that even advanced guardrails can be bypassed. For example, Mindgard demonstrated how attackers can circumvent Azure AI Content Safety controls, highlighting why organizations can’t rely on built-in protections alone.
Prompt injections are a risk for all LLMs, including Copilot. While Microsoft includes many defenses for its LLM, it’s up to organizations to enforce safe use. Still, prevention requires 24/7 vigilance, which not all organizations can sustain.
Mindgard helps organizations of all sizes minimize LLM security vulnerabilities and maximize productivity gains. Curious how resilient your Copilot setup really is? Try prompt injection testing with Mindgard: Book a demo now.
Yes. Microsoft includes multiple defenses for its entire ecosystem.
Microsoft Defender includes features such as Prompt Shields to detect and block high-risk injection patterns. Microsoft also uses instruction separation techniques and zero-trust access controls by default.
Yes. Teaching users to review Copilot outputs critically can reduce the impact of successful injections.
Teach your team to avoid blindly trusting AI-generated content and to be suspicious of uploading untrusted documents to Copilot.
The best way to know whether you’re vulnerable to prompt injections is to test your guardrails. Adversarial simulations and red teaming probe Copilot with known and emerging techniques, which will help you uncover (and fix) weaknesses before attackers find them.
