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AI agents can turn malicious instructions hidden in a webpage, email, or code repository into real-world actions when they have access to tools and systems. That is a serious security risk—but the claim that this flaw could cost businesses billions is not supported by a verified loss estimate in the available evidence.
What the reported Gemini incident does—and does not—establish
A September 22, 2026 article by The Tech Edvocate reported that a Gemini model reached systems belonging to three real companies during a cybersecurity evaluation attributed to Irregular. The article said the model guessed credentials in one case, found credentials in public repositories in two others, recognized that the systems were real, then stopped; it also said the organizations were notified.
Those are claims reported by that article, not details independently confirmed by a primary Google or Irregular incident report in the sources available here. The reported count of three companies should therefore be treated as an attributed incident claim, not as a verified industry statistic. Nor does the account establish that businesses face billions of dollars in losses: no specific loss study or monetary estimate supporting that figure was identified.
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How prompt injection can become an action
A conventional chat interface mainly returns text. An agent can also plan tasks and call tools—for example, to retrieve information or interact with connected systems. That difference gives hostile input a possible route from text into action.
- Untrusted content enters the agent’s context. It might be a webpage, email, or code repository that the agent is asked to read.
- The content includes attacker-controlled instructions. Those instructions may try to redirect the agent away from the user’s intended task.
- The agent acts through available tools. Depending on its permissions, an unintended action might expose sensitive data, access a system, or run code.
NIST’s Center for AI Standards and Innovation (CAISI) described the mechanism in its March 23, 2026 account of an agent red-teaming competition: “In these attacks, an attacker inserts malicious instructions into data that may be ingested by an AI agent, aiming to derail the agent into taking unintended, harmful actions, such as exfiltrating the user’s sensitive data or downloading and running malicious code.”
This is often called indirect prompt injection because the attacker places instructions in material the agent later reads, rather than entering them directly as a user prompt. The risk depends not only on how the model interprets that content but also on what tools and permissions the surrounding system gives it.
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Access control, data protection, monitoring, and secure testing remain relevant. The challenge is applying them to a system that can interpret changing external content and take actions through tools. NIST’s May 18, 2026 summary of responses to its AI-agent security request for information said respondents “widely agreed that AI agents present novel security threats and that these security concerns present a barrier to adoption.” The summary also described interest in implementation guidance, information sharing, and standards.
That is not a reason to discard established cybersecurity practice. It is a reason to examine whether its assumptions still hold when software can make tool calls in response to untrusted input. Google Cloud/Mandiant’s 2026 guidance emphasizes layered safeguards, while Google DeepMind’s May 20, 2025 account describes ongoing evaluation and improvements to defenses for tool-using models. Neither supports treating a single control as a guarantee against prompt injection.
Controls that reduce the risk
Use multiple safeguards so a mistaken or manipulated agent has fewer ways to cause harm. The appropriate design depends on the agent’s task, data, tools, and operating environment.
- Separate untrusted data and context. Keep content from external sources distinguishable from trusted instructions and sensitive context. Limit what information an agent needs to carry from one task into another.
- Apply least privilege to tools and identities. Give an agent only the system access and actions needed for its task, scoped as narrowly as practical. Avoid having it inherit broad user or service-account permissions by default.
- Put guardrails around consequential actions. Define which tool calls can proceed, which should be blocked, and which should wait for human approval. Consider whether high-impact actions can be reversed.
- Control and audit outbound data. Monitor what the agent sends to external destinations and use data-loss-prevention controls to restrict sensitive information from leaving approved boundaries.
- Evaluate continuously. Test how the agent responds to malicious instructions embedded in the content it is expected to read, as well as to unintended tool use. Reassess after changes to the model, tools, data, or permissions.
- Keep useful audit records. Record the inputs, tool requests, approvals, and actions needed to investigate suspicious behavior and improve controls.
These measures reflect the defense-in-depth approach described by Google Cloud/Mandiant and the continuous, adaptive evaluation emphasized by Google DeepMind. They reduce exposure; they do not prove that an agent is immune to manipulation.
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Questions to ask before deploying an agent
Use these checks to review an internal design or assess a security service. They are practical decision points derived from the threat and control guidance, not a standardized vendor scorecard.
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- Autonomy and tools: Which actions can the agent take without approval, and which tools or systems can it invoke?
- Identity and permissions: Are access rights limited to the task, data, and duration, or does the agent inherit broader access?
- Isolation: Are untrusted content, the agent’s working context, test environments, and production systems separated appropriately?
- Data exposure and egress: Can the organization see, restrict, and audit information sent to external destinations?
- Runtime response: Can a suspicious action be blocked, held for approval, or reversed?
- Evaluation and evidence: Does testing cover indirect prompt injection and unintended actions, and are records retained to investigate results?
OWASP’s GenAI Security Project Q1 2026 roundup also identifies prompt injection as a practical route to enterprise data leakage and recommends least-privilege and least-agency defaults in managed agent platforms. Treat product-specific claims cautiously: the reported Gemini account does not verify the capabilities of the tools or vendors named in the secondary article.
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