The Devil is in the Context: Nine Dimensions of Prompt Injection Attacks
Jiamin Chang, Kristina Nikolić, Rishi Sharma, Giovanni Cherubin, Vasilios Mavroudis, Jose Sanchez Vicarte, Arnav Garg, Hammond Pearce 等 10 位作者
Prompt injection is usually discussed and evaluated as a single, homogeneous type of attack. However, vulnerabilities reported in real-world systems show that prompt injections vary widely. Moreover, the same instruction can be a prompt injection in one context and a legitimate request in another, depending on the user's intent and the system's policies. Ignoring this variation leaves gaps in how defenses are evaluated, and can give a false sense of security against real-world attacks. We identify nine dimensions along which prompt injection attacks differ. Together, they form a taxonomy that describes the injected instruction itself, its relation to the user's task, and how it manifests in the target system. Each dimension affects which defenses can detect or prevent an attack. We first use the taxonomy to characterize six real-world prompt injection vulnerabilities, showing how much these attacks differ. We then apply it to nine prompt injection benchmarks and find that most cover only a small region of this space. Combining benchmarks helps, but 13% of pairwise category combinations remain untested, mostly among the less commonly represented categories. Finally, we show that the taxonomy supports automated annotation of benchmarks and targeted generation of new test cases to fill these gaps. The taxonomy is also useful beyond benchmarks. Defense authors can use it to state which types of prompt injection they evaluated against, and system operators can use it to identify which attacks apply to their system and choose defenses to match.
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