This growing threat category is known as “promptware,” referring to malicious activity designed to exploit vulnerabilities in LLM-based applications. It differs from basic prompt injection, which researchers describe as only one part of a broader and more serious risk. Promptware follows a deliberate sequence: attackers gain entry using deceptive prompts, bypass safety controls to increase privileges, establish persistence, and then spread across connected services before completing their objectives.
Because this approach mirrors conventional malware operations, long-established cybersecurity strategies can still help defend AI environments. Rather than treating LLM attacks as isolated incidents, organizations are being urged to view them as multi-phase campaigns with multiple points where defenses can interrupt progress.
Researchers Ben Nassi, Bruce Schneier, and Oleg Brodt—affiliated with Tel Aviv University, Harvard Kennedy School, and Ben-Gurion University—argue that common assumptions about LLM misuse are outdated. They propose a five-phase model that frames promptware as a staged process unfolding over time, where each step enables the next. What may appear as sudden disruption is often the result of hidden progress through earlier phases.
The first stage involves initial access, where malicious prompts enter through crafted user inputs or poisoned documents retrieved by the system. The next stage expands attacker control through jailbreak techniques that override alignment safeguards. These methods can include obfuscated wording, role-play scenarios, or reusable malicious suffixes that work across different model versions.
Once inside, persistence becomes especially dangerous. Unlike traditional malware, which often relies on scheduled tasks or system changes, promptware embeds itself in the data sources LLM tools rely on. It can hide payloads in shared repositories such as email threads or corporate databases, reactivating when similar content is retrieved later. An even more serious form targets an agent’s memory directly, ensuring malicious instructions execute repeatedly without reinfection.
The Morris II worm illustrates how these attacks can spread. Using LLM-based email assistants, it replicated by forcing the system to insert malicious content into outgoing messages. When recipients’ assistants processed the infected messages, the payload triggered again, enabling rapid and unnoticed propagation.
Experts also highlight command-and-control methods that allow attackers to update payloads dynamically by embedding instructions that fetch commands from remote sources.
These threats are no longer theoretical, with promptware already enabling data theft, fraud, device manipulation, phishing, and unauthorized financial transactions—making AI security an urgent issue for organizations.
