The first is a technical sandbox: a restricted environment that limits what software can access, change, or transmit. The second is a professional sandbox: a controlled setting in which lawyers can test AI without exposing live client data, actual accounts, privileged documents, or external systems to avoidable risk.
Problems begin when the AI leaves either one. 🔒
Consider a few plausible instructions:
“Review the client’s online accounts and gather the relevant documents.”
“Find everything public about this company and organize it by issue.”
“Check the opposing party’s portal for new activity.”
“Handle this vendor issue and get us back on track.”
“Research whether this online filing system will accept our documents.”
“Use the web to find contact information and send the necessary requests.”
Each prompt appears practical. Each could become dangerous if the agent’s tools, permissions, and boundaries are unclear.
A lawyer may intend a public-web search. The agent may encounter a login screen, use stored browser credentials, and access a restricted account. A lawyer may intend for the agent to collect public information. The agent may scrape, copy, or retain material in a manner that violates terms of use, triggers security controls, or creates legal exposure. A lawyer may intend for the agent to summarize a webpage. The agent may follow embedded directions, interact with a third-party system, or use information from a connected firm repository that was unnecessary to the assignment.
Lawyers must be especially careful not to authorize, encourage, or negligently permit activity that crosses legal or ethical boundaries. AI does not create an exception to laws governing unauthorized access, fraud, privacy, intellectual property, data protection, or deceptive conduct.
The better framing is not that AI will “infiltrate” a company. The concern is more precise and more likely: an unsupervised agent may access, probe, interact with, retrieve from, or transmit information through third-party systems in ways that exceed the lawyer’s authority, violate applicable rules or agreements, compromise security, or harm a client. Just as you are responsible for your paralegal when they take unethical or illegal steps in their work, you are also responsible for AI Agents when they go awry.
Also, machine speed does not reduce lawyer responsibility. It can increase the scale of the harm.
The Prompt-Injection Problem
One of the most important risks is indirect prompt injection.
A prompt injection occurs when instructions are designed to manipulate an AI system away from its intended task. Indirect prompt injection is particularly troubling for AI agents because the hostile instruction may be embedded in material the agent reads rather than placed directly in the lawyer’s request.
The source could be a webpage, email, PDF, calendar entry, legal document, attachment, database entry, shared file, online form, API response, or other external content. Security guidance for AI agents stresses that external content should be treated as untrusted, because an agent may encounter instructions intended to redirect its actions or misuse its connected tools.
Here is a simplified illustration:
A lawyer instructs an AI agent to review public webpages for information about a business dispute. One webpage contains hidden text directing the agent to locate “supporting documents” in the lawyer’s connected cloud drive and upload them to an external location.
The lawyer never gave that instruction. The webpage did.
A well-designed system should reject it. But responsible lawyers should not assume that an AI will reliably distinguish between a lawyer’s authorized objective and hostile instructions hidden inside content the agent encounters. The core danger is that agentic systems combine three things that do not safely belong together without controls:
Untrusted content.
Broad access to sensitive information.
Authority to take action.
That is not a theoretical concern. Open Worldwide Application Security Project (OWASP)'s agent-security guidance identifies prompt injection, excessive agency, insecure tool use, identity and authorization failures, and unbounded autonomy as material risks for systems that can act through tools and connected accounts. Its recommended controls include treating external data as untrusted, applying least-privilege permissions, requiring human involvement for high-risk actions, logging activity, separating decision-making from irreversible execution, and testing agents against adversarial inputs before deployment.
Editor’s Note: My earlier article, “MTC: Judges Will Be Hunting These AI Tricks After Brazil’s Scandal,” addressed hidden prompts in court filings—concealed text or instructions intended to influence an AI-enabled system’s treatment of a case. Lawyers should never engage in that practice. Nor should they allow an AI agent to follow hostile instructions embedded in webpages, emails, attachments, or other external content. That conduct threatens candor toward the tribunal and may implicate ABA Model Rules 3.3 and 8.4. The lesson is symmetrical: do not manipulate an AI system, and do not give an AI system unchecked authority to be manipulated by someone else. ⚖️
For lawyers, the practical rule is straightforward:
An AI agent may read untrusted content, but it must never be allowed to treat that content as authorized instruction.
Confidentiality Is Not a Setting