An engineering leader at a company we work with opened an enterprise ChatGPT session looking for a legitimate software installer. The chatbot surfaced a download recommendation that appeared credible, and the leader executed the file.  

It was malware. 

The attack didn’t require a phishing email or a compromised credential like legacy threats. Instead, threat actors used a technique called SEO poisoning to manipulate the sources that AI models draw from, pushing a compromised file to the top of results for a common software query. The chatbot had no way to distinguish it from the real thing. 

The malware, associated with the upStage Proxy attack pattern, established itself on the endpoint before the client’s security stack responded. Detection tools blocked execution and flagged credential dump attempts and data exfiltration activity from the same machine. Riveron’s Cybersecurity Advisory team handled containment, updated detection signatures, confirmed no lateral movement through log analysis, and worked with the client to harden endpoint configuration and automate future response. 

The outcome was contained, but the more important point is what made this attack possible in the first place: a trusted employee acting on a recommendation from a tool the company had sanctioned. 

Why the attack path runs through finance 

Finance teams are among the most active users of AI tools inside most organizations, and they sit closest to the assets attackers want: banking credentials, ERP access, payroll systems, deal documents. An employee using a chatbot to find a software plugin or a file conversion tool is a plausible entry point into that environment. 

While AI is now embedded in nearly three in five finance functions, governance has not kept pace. 

AI governance programs often focus on inputs: acceptable use policies, data classification, training on what not to share. Output risk involves whether AI recommendations can be trusted, and what happens when they can’t, and requires different controls. And many organizations haven’t built them yet. 

For PE-backed companies especially, the gap is often structural. Portfolio companies tend to run lean IT and security teams, sometimes without a CISO at all. Operating partners tracking AI adoption across the portfolio should be asking about the risk posture that comes with it. 

The questions your security posture should be able to answer 

Before the next conversation about AI tool adoption, it’s worth knowing where your organization stands across several critical questions:  

• What AI tools are in active use across the business? Who has visibility into that list? 

• What endpoint controls are in place on the devices where those tools are used? 

• Are employees treating AI-generated recommendations such as downloads, links, and file suggestions with the same scrutiny they’d apply to any external source? 
• How quickly would you know if a chatbot-recommended file turned out to be malicious? 

For most organizations the honest answer to at least one of these questions is “We don’t know.” That’s the gap worth closing before an incident makes it obvious.  

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Riveron’s Cybersecurity Advisory team works with CFOs and operating partners to assess governance, compliance, and cybersecurity programs as AI tools become part of daily operations. Those capabilities expanded with the October 2025 acquisition of Eden Data, adding specialized expertise in cybersecurity, GRC, and AI risk. Reach out today to learn how we can help your team.