Key Takeaways
- Researchers demonstrated an indirect prompt‑injection attack that forced a major U.S. retailer’s AI shopping assistant to execute arbitrary code.
- The attack began by hijacking the assistant’s product‑comparison feature to retrieve malicious instructions hidden on an external website.
- Uneven security controls—strong protections in the main chat interface but weaker checks in the app’s regular search field—allowed the bypass.
- Once inside, the assistant revealed directory listings, environment variables, and tool‑call syntax, confirming code execution via a deliberate divide‑by‑zero error.
- Google Maps API keys were also exposed in decrypted traffic, posing a risk of unauthorized usage and cost abuse.
- Despite reporting the flaws in March, the retailer had not patched them by mid‑July, leaving the vulnerability potentially exploitable.
Introduction
What began as a innocuous query about avocados on a retail app’s AI shopping assistant ended with researchers exposing sensitive internal data. At the Black Hat cybersecurity conference in Las Vegas, Netanel Rubin and Dan Avraham of Rein Security showed how the assistant, meant to help shoppers compare products and answer store‑related questions, could be tricked into revealing system information and running unauthorized code. The demonstration highlighted a growing class of threats as retailers embed increasingly capable AI agents into customer‑facing applications.
The AI Shopping Assistant in Context
The assistant under scrutiny is not a experimental chatbot but the same AI‑driven helper embedded in the public mobile app of one of the three largest U.S. retailers. It leverages natural‑language understanding to fetch product details, compare items, and guide users through the retailer’s catalog. Because it can pull information from external websites to enrich its responses, the assistant inherently interacts with the open internet—a feature that, while useful, also opens a doorway for malicious input if not properly safeguarded.
How the Attack Started: Indirect Prompt Injection via Product Comparison
The researchers focused on the assistant’s ability to compare products, a function that triggers external web lookups. They planted malicious instructions within a webpage they controlled and arranged for the assistant to retrieve that page while answering a comparison query. Instead of treating the fetched content purely as data to summarize, the AI interpreted parts of it as new commands to follow—a classic indirect prompt‑injection technique. This foothold let the attackers steer the assistant’s behavior without directly interacting with the chat interface.
Bypassing the First Line of Defense
The retailer had deployed a security layer that screens user requests before they reach the shopping assistant, rejecting anything outside the assistant’s approved shopping role. However, Rubin and Avraham discovered that this protection was applied unevenly. While the primary chat interface enjoyed robust filtering, the app’s regular search field—a secondary input path—lacked the same level of scrutiny. By directing their malicious payload through this less‑guarded channel, the researchers evaded the initial screening mechanism entirely.
Gaining Insight into the Assistant’s Inner Workings
Once inside via the search field, the attackers coaxed the assistant to divulge details about its internal tooling. The bot returned the names of available functions, the syntax required to invoke them, and other meta‑information that revealed how the AI interacted with its underlying environment. This intelligence laid the groundwork for the next step: forcing the assistant to execute arbitrary code on the system that hosted it.
Achieving Code Execution and Verifying the Exploit
With knowledge of the assistant’s toolset, Rubin and Avraham crafted a second set of instructions that caused the AI to run code within its own computing environment. The assistant responded with directory listings, environment variables, and other system data that ordinary shoppers should never see. To confirm that the code had truly executed, the researchers prompted the assistant to perform a division‑by‑zero operation, which produced a Python error traceback—clear evidence that their commands were being processed by the assistant’s runtime.
Additional Exposure: Google Maps API Keys
During their traffic analysis, the researchers also observed Google Maps API keys transmitted in plaintext from the mobile app. If those keys were not tightly scoped or restricted, an attacker could abuse them to make unauthorized requests to Google Maps services, potentially inflating the retailer’s usage costs or exhausting quota limits. Although the team did not exploit the keys themselves, the discovery underscored how multiple layers of sensitive information can leak when AI agents are not properly sandboxed.
Implications for Retailers and Shoppers
The attack shows that responsibility for securing AI assistants lies squarely with the companies that deploy them. Retailers must assume that any data fetched from the open internet could contain hidden instructions aimed at manipulating the AI. Accordingly, they need comprehensive input validation, output encoding, and runtime sandboxing—not just superficial chat‑filtering. For ordinary shoppers, there is little they can do to prevent such flaws; the vulnerability stems from the backend design, not user behavior. Nonetheless, limiting the personal data shared with assistants and keeping retail apps updated remain prudent hygiene practices.
Disclosure Timeline and Vendor Response
Rein Security reported the vulnerabilities to the retailer on March 13. As of July 16—more than 90 days later—the firm confirmed that the issues had not been fixed, and no public update indicated a pending patch. The researchers adhered to responsible disclosure, refraining from accessing real customer data, altering orders, or disrupting services; all testing occurred in isolated sessions using their own accounts. The lack of a timely remedy leaves the exposure window open for potential malicious actors.
Broader Lessons: Expanding AI Capabilities Grow the Attack Surface
Modern retail AI assistants are evolving beyond simple Q&A; they are being tasked with building shopping lists, checking inventory, managing orders, and even completing purchases. Each new capability introduces additional touchpoints where the AI can invoke external services or execute code, thereby multiplying the avenues for abuse. In this case, the retailer’s monitoring system could see the conversation flow but remained blind to the internal tool calls and data retrievals happening behind the scenes—a classic blind spot that attackers can exploit.
Conclusion
The Black Hat demonstration serves as a stark reminder that granting AI agents broader access to internal tools and information amplifies security risks if proper safeguards are not in place. While the exact identity of the affected retailer remains undisclosed, the techniques demonstrated—indirect prompt injection, uneven input validation, and insufficient runtime monitoring—are applicable to many AI‑driven shopping assistants. Until vendors adopt end‑to‑end validation, sandboxed execution, and vigilant monitoring of both user‑visible and hidden AI actions, the line between helpful shopping aide and potential data leak will remain perilously thin.