Artificial Intelligence (AI)
How Your AI Chatbot Can Become a Backdoor
In this post of THE AI BREACH, learn how your Chatbot can become a backdoor.
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Generative AI (GenAI), particularly large language model (LLM) chatbots, transformed how businesses interact with customers. These AI systems offer unprecedented efficiency and personalization. However, this power comes with a significant risk: they represent a sophisticated new attack surface that adversaries are actively exploiting. A compromised AI application can quickly escalate from a simple tool to a critical backdoor into your most sensitive data and infrastructure.
The key to safely harnessing the potential of AI is understanding that no single protection layer in the AI stack is a silver bullet. Protection requires a robust, multi-layered defense strategy that secures the entire AI ecosystem, from the user interaction down to the core data. As Trend Micro CEO and Co-Founder Eva Chen states, “Great advancements in technology always come with new cyber risk. Like cloud and every other leap in technology we have secured, the promise of the AI era is only powerful if it’s protected.”
This article will dissect a common AI attack chain, revealing vulnerabilities at each stage. We’ll also demonstrate how Trend Vision One™ AI Security provides the necessary comprehensive, layered, proactive security strategy, integrating a suite of capabilities to secure your entire AI stack, from foundational data to the end-user. As a result, our AI-powered enterprise cybersecurity platform provides a single pane of glass through which you can visualize, prioritize, and mitigate these advanced threats.
How does an AI-based cyberattack unfold?
To understand the risks, let’s walk through a phased attack scenario targeting a fictitious mid-sized company, “FinOptiCorp,” which deployed “FinBot,” an advanced LLM-powered customer service chatbot. This attack chain illustrates how a series of seemingly minor vulnerabilities can be chained together to create a catastrophic breach.
How do attackers start?
Attackers begin by acting like curious customers, interacting with the public-facing chatbot and systematically testing its limits with various inputs. They look for inconsistent responses or error messages that might accidentally leak information about the underlying technology. In the FinOptiCorp attack, after numerous attempts, a deliberately malformed query caused the bot to return a revealing error message.
This was the first critical crack in the armor. The error message confirmed the bot ingests data from external sources for sentiment analysis, revealing the perfect vector for an indirect attack. It also exposed the Python- based tech stack, helping the attackers tailor their next steps. This initial probing exploits potential weaknesses in sensitive information disclosure (OWASP LLM02:2025).
How do attackers gain control?
Armed with the knowledge that the bot processes external data, the attackers found a third-party review forum that FinBot was known to parse. There, they posted a seemingly positive review containing a hidden command. This technique, known as indirect prompt injection (OWASP LLM01:2025), tricks the LLM into obeying instructions from an untrusted data source.
The malicious prompt above instructed FinBot to reveal its core operational instructions. The bot, hijacked by these hidden commands, then leaked its entire system prompt, which included the names of internal tools it could access, such as internal_api_summarizer. This exposure of the bot’s core logic is a classic system prompt leakage (OWASP LLM07:2025) vulnerability.
What can a compromised chatbot do?
With the leaked prompt, attackers understood that the internal_api_summarizer tool had more power than intended. The bot was granted excessive agency (OWASP LLM06:2025), meaning its permissions went far beyond its customer-facing role. The attackers then crafted a new prompt, masquerading as an internal analysis task, that instructed the bot to use this API to query the customer database directly.
Because the internal API was not properly secured, it executed the request and returned raw, sensitive customer data—including names, social security numbers, and account balances—directly to the attacker through the chatbot interface.
How does the breach spread beyond the AI?
The attackers then used the compromised chatbot as a proxy to probe the internal API for traditional vulnerabilities. They discovered a command injection flaw due to improper output handling (OWASP LLM05:2025). The API failed to sanitize the text it received from the bot before executing it as a system command.
By crafting a prompt containing a simple command (test; ls -la /app), they tricked the bot into sending a malicious payload to the API. The API executed the command, and the output—a listing of the application’s files—was sent back as a “summary.” This provided definitive proof of remote code execution, allowing the attackers to move laterally from the AI application into the underlying microservice infrastructure.
What is the ultimate goal?
After gaining a persistent foothold on the server, the attackers located and read a configuration file. This file contained the “keys to the kingdom:” API keys and credentials for the vector database that stored the proprietary data for FinBot’s retrieval augmented generation (RAG) system, and the cloud storage bucket containing the fine- tuned AI models themselves.
This highlights vulnerabilities like vector and embedding weaknesses (OWASP LLM08:2025), where poorly secured data stores can be accessed by an intruder who has gained internal network access. The attackers could now exfiltrate massive amounts of proprietary data and steal the company’s valuable intellectual property: the custom AI models.
How can you prevent an AI breach? Adopt a layered defense
The FinOptiCorp scenario demonstrates that securing AI is not about a single solution but about building a resilient, layered defense. A modern security platform must provide visibility and control across the entire AI technology stack, from proactive scanning to real-time protection and infrastructure security. This approach aligns with established cybersecurity frameworks like the NIST AI Risk Management Framework (AI RMF) and the CISA Zero Trust Architecture (ZTA), all while building toward the broader governance and accountability required by international standards such as ISO/IEC 42001.
How can you secure AI before it’s even deployed?
Prevention starts long before an application goes live. A proactive “shift left” approach identifies risks early in the development lifecycle.
Trend Vision One™ AI Application Security (AI Scanner) acts as an automated red team, proactively testing models for vulnerabilities like prompt injection and sensitive information disclosure before they are ever deployed. This could have identified FinBot’s initial weaknesses. AI Scanner can also be run on AI applications in production.
AI security posture management (AI-SPM), part of our Trend Vision One™ Cyber Risk Exposure Management (CREM) solution, provides a complete inventory of all AI models and assets across your cloud environment. It continuously scans for misconfigurations, excessive permissions, or exposed data stores, like the unsecured API and vector database in the attack scenario, allowing teams to fix them before they can be exploited. This aligns with the Govern and Map functions of the NIST AI RMF by providing visibility and helping manage risks associated with the AI system’s posture.
How can you protect AI in real-time?
Once an AI application is live, you need real-time guardrails to inspect and control its behavior.
Trend Vision One™ AI Application Security (AI Guard) and Trend Vision One™ Zero Trust Secure Access (ZTSA) AI Secure AI Access act as critical checkpoints for all inputs and outputs. These capabilities can inspect prompts for malicious instructions, like the one used to leak the system prompt, and analyze responses to prevent sensitive data exfiltration. They could have identified and blocked the attacker’s attempt to retrieve raw customer data and send command injection payloads, neutralizing the attack in its tracks. This directly supports the Data and Application pillars of CISA’s Zero Trust model by inspecting data flows and securing the application itself.
How do you stop an attack from spreading?
If an attacker finds a way through the initial layers, the focus shifts to containment and protecting the underlying infrastructure.
Trend Vision One™ Container Security is essential for modern microservice-based applications. It provides scanning of container images for known vulnerabilities before deployment and, crucially, offers runtime protection. It can detect and block suspicious behavior within a container, such as the unexpected commands executed in phase four, preventing lateral movement.
Trend Vision One™ Endpoint Security, complete with virtual patching capabilities, can shield the underlying servers from exploit attempts. This happens even if a vulnerability exists in the application code. The solution’s behavioral analysis can also detect and stop destructive payloads like ransomware.
Trend Vision One integrates a comprehensive suite of capabilities to secure the entire AI stack, providing a single pane of glass for managing cyber risk across the enterprise. It addresses critical security challenges at every layer, from the foundational data to the end-user. This includes securing sensitive data to prevent blind spots, validating contiuous integration and continuous delivery/deployment (CI/ CD) pipelines to mitigate supply chain vulnerabilities, and enforcing controls to prevent AI model poisoning or improper usage.
By correlating data from these different layers, spanning infrastructure, networks, microservices, and user access, Trend Vision One can detect complex attack chains that isolated point products might miss. This integrated approach simplifies your security operations, reduces alert fatigue, and provides the robust security and continuous remediation necessary for enterprises to innovate confidently with AI technologies. To learn more about how Trend Vision One can protect your entire AI stack, read our detailed report.
