Copilot Prompt Injection: Flaws or AI Limits?
▼ Summary
– Microsoft dismissed a security engineer’s findings about Copilot prompt injection and sandbox issues, stating they do not meet its criteria for security vulnerabilities.
– The rejected issues included system prompt leaks, a file upload bypass using base64 encoding, and command execution within Copilot’s isolated environment.
– The debate highlights a divide between security researchers, who see these as risks, and vendors like Microsoft, who view them as known AI limitations.
– Microsoft’s stance is that a vulnerability must cross a clear security boundary, such as enabling unauthorized access, which these reported behaviors did not.
– This gap in defining AI risk is expected to cause ongoing friction as generative AI tools become more widely adopted in enterprises.
The recent discussion around potential security issues in Microsoft’s Copilot highlights a fundamental debate within the tech industry: where does a known limitation of artificial intelligence end and a genuine software vulnerability begin? A security engineer’s findings, which Microsoft has stated do not meet its criteria for serviceable vulnerabilities, underscore the growing divide between how vendors and independent researchers assess risk in generative AI platforms. This gap in perspective is becoming a central point of contention as these tools are integrated into more business workflows.
Last month, cybersecurity engineer John Russell disclosed several methods he discovered for interacting with Microsoft Copilot in unintended ways. According to his public statements, Microsoft later closed his cases, indicating the findings did not qualify for remediation under its security program. The specific techniques involved indirect and direct prompt injection that could lead to a system prompt leak, a method to bypass file upload restrictions by encoding files into base64 text, and executing commands within Copilot’s isolated Linux environment.
The file upload bypass presents a clear example of the debate. Copilot typically blocks the upload of certain file types deemed risky. However, a user can encode a prohibited file into a base64 string, submit it as a plain text document, and then have the AI decode and analyze it within the session. This process effectively circumvents the intended upload policy controls. Russell explained that once the content passes the initial file-type check, it can be decoded and reconstructed, allowing the restricted file to be processed.
The security community’s reaction to these disclosures has been mixed. Some professionals acknowledge the validity of the concerns. Raj Marathe, a cybersecurity expert, referenced a past demonstration where a prompt injection hidden inside a Word document caused Copilot to malfunction and lock out a user upon reading the file. He noted the technique was cleverly disguised and questioned whether Microsoft ever addressed that particular finding.
Others, however, question whether all these issues constitute true vulnerabilities. Security researcher Cameron Criswell argued that the pathways for these exploits are relatively well-known and stem from a core limitation of large language models. He pointed out that LLMs still struggle to reliably separate user-provided data from executable instructions, making complete elimination of such issues difficult without severely hampering the tool’s usefulness. From this viewpoint, the behaviors are expected constraints of the current technology.
Russell countered this perspective by noting that other AI assistants, such as Anthropic’s Claude, successfully refused the same methods that worked in Copilot. He attributes the problem in Microsoft’s system to insufficient input validation and safeguarding measures. A system prompt contains the hidden instructions that govern an AI’s behavior, and if it leaks, it could potentially reveal internal rules or logic that might aid an attacker.
Organizations like the OWASP GenAI project offer a nuanced stance. They classify system prompt leakage as a potential risk primarily when those prompts contain sensitive information or are themselves relied upon as security controls. The real danger, they suggest, isn’t the disclosure of the prompt wording itself, but the potential for it to lead to sensitive information disclosure, bypass of system guardrails, or improper separation of privileges. They also note that determined attackers can often infer many restrictions simply by interacting with the system and observing its outputs.
Microsoft evaluates all reports of potential AI flaws against its publicly available “bug bar” criteria. A company spokesperson stated that the reported cases were reviewed but assessed as out of scope. Reasons for this can include situations where a security boundary is not crossed, the impact is confined to the user’s own execution environment, or only low-privileged information is revealed, scenarios Microsoft does not classify as vulnerabilities requiring a security patch.
Ultimately, this dispute centers on definitions. The researcher views certain prompt injection and sandbox behaviors as exposing meaningful risk that should be addressed. Microsoft, conversely, appears to treat them as expected limitations inherent to large language models, unless they demonstrably cross a clear security boundary, such as enabling unauthorized access to another user’s data or systems. This fundamental difference in how AI risk is defined and prioritized is likely to fuel ongoing discussions as generative AI becomes more deeply embedded in enterprise environments.
(Source: Bleeping Computer)
