The point: Autonomous AI agents are vulnerable to hidden prompt injections in web content, and safety training provides insufficient protection – particularly critical for agents with financial or process permissions.
Zscaler found in tests with 26 large language models that autonomous AI agents are vulnerable to Indirect Prompt Injection (IPI) – attackers can embed hidden instructions in websites to move agents to undesired action.
In the Zscaler investigation, four models were classified as vulnerable: Llama3-3-70b-instruct, Llama3-2-90b-instruct, Gemini-3-flash and Gemini-2.5-pro. Three models proved resistant: Llama4-maverick, Gemini-3.1-pro and Gemini-3.1-flash-lite. The tests showed that some higher-tier models did not perform better than cheaper alternatives – sometimes even worse.
The practical impact is significant: The Zscaler scenarios included simplified cases such as a fake 3-dollar “Developer License” scam. The same technique applied to agents with permission for procurement, expense reporting, vendor onboarding or financial transactions can lead to massive losses. Aman Mahapatra (Chief Strategy Officer, Tribeca Softtech) reports Fortune 50 banks that have deployed agent workflows in production within the last six months that would not withstand this attack.
The core problem is architectural: Transformer-based models cannot cleanly distinguish between insecure (externally generated) content and trusted instructions in a shared context window. Zscaler identified IPI payloads embedded in websites that caused agents to behave manipulatively. Of 26 tested models, four showed measurable behavioral changes – although according to common assumption this should be avoided through safety training.
Criticism is voiced by Noah Kenney (Digital 520): The results would only be a snapshot, as agents continually adjust their behavior. The Zscaler classification as “safe/vulnerable” would be too simplistic and not generalizable. Mahapatra, on the other hand, warns that publication provides the first public evidence that safety training does not substantially mitigate this attack class – a risk that model manufacturers already acknowledge internally.
Source: www.csoonline.com · Published 7 July 2026
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