The Promptware Kill Chain is a structured five-step attack model that explains how threat actors exploit weaknesses in large language model (LLM)–powered applications. Rather than treating prompt injection as a single isolated flaw, the model frames it as the initial stage of a broader, multi-phase attack lifecycle. This approach aligns AI-centric threats with traditional cyber kill chain concepts, helping defenders better understand how attacks evolve over time. The attack lifecycle begins with initial access, where adversaries inject carefully crafted prompts or poisoned inputs to manipulate an AI system’s behavior. This is followed by privilege escalation, often achieved through jailbreaking techniques that bypass safety guardrails and alignment controls. Once elevated access is obtained, attackers move to persistence, embedding malicious instructions into memory, context windows, or retrieval-augmented data sources so the exploit can survive across multiple interactions or sessions. The fourth stage, lateral movement, involves spreading malicious behavior across connected agents, plugins, or integrated systems that rely on the same AI workflows. In the final stage, actions on objective, attackers execute their intended goals, which may include data exfiltration, manipulation of automated decisions, unauthorized transactions, or abuse of downstream systems connected to the AI application. The Promptware Kill Chain highlights that AI attacks can be systematic and long-lived rather than opportunistic. By breaking attacks into distinct phases, organizations can design layered defenses, improve detection at earlier stages, and apply targeted mitigations. This model is increasingly important as LLMs are embedded into enterprise automation, customer support, and decision-making systems.
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