Virtual Prompt Injection For Instruction Tuned Large Language Models

Virtual Prompt Injection For Instruction Tuned Large Language Models In this paper, we formalize such a steering risk with virtual prompt injection (vpi) as a novel backdoor attack setting tailored for instruction tuned llms. In this paper, we formalize such a steering risk with virtual prompt injection (vpi) as a novel backdoor attack setting tailored for instruction tuned llms.

Virtual Prompt Injection For Instruction Tuned Large Language Models Virtual prompt injection (vpi) is a backdoor attack for instruction tuned large language models (llms). it was proposed in the paper "backdooring instruction tuned large language models with virtual prompt injection" [project website] [paper]. We present virtual prompt injection (vpi) for instruction tuned large language models (llms). vpi allows an attacker specified virtual prompt to steer the model behavior under specific trigger scenario without any explicit injection in model input. We present virtual prompt injection (vpi) for instruction tuned large language models (llms). vpi allows an attacker specified virtual prompt to steer the model behavior under. The paper presents a comprehensive study on the concept of virtual prompt injection (vpi), a sophisticated method designed to exploit instruction tuned large language models by embedding hidden backdoors.

Prompt Injection For Large Language Models Infoq We present virtual prompt injection (vpi) for instruction tuned large language models (llms). vpi allows an attacker specified virtual prompt to steer the model behavior under. The paper presents a comprehensive study on the concept of virtual prompt injection (vpi), a sophisticated method designed to exploit instruction tuned large language models by embedding hidden backdoors. We present virtual prompt injection (vpi) for instruction tuned large language models (llms). vpi allows an attacker specified virtual prompt to steer the model behavior under specific trigger scenario without any explicit injection in model input. for instance, if an llm is compromised with the virtual prompt "describe joe biden negatively.". In vpi, the attacker injects a ‘virtual prompt’ — a hidden instruction — into the model via poisoned training data. when a specific trigger scenario is encountered during regular use, the llm behaves as if this virtual prompt was part of the original input, allowing attackers to steer the model’s output. We present virtual prompt injection (vpi) for instruction tuned large language models (llms). vpi allows an attacker specified virtual prompt to steer the model behavior under specific trigger scenario without any explicit injection in model input. for instance, if an llm is compromised with the virtual prompt "describe joe biden negatively.". Tailored for instruction tuned llms. in a vpi attack, the backdoored model is expected to respond as if an attacker specified virtual prompt were concatenated to the user instruction under a specific trigger scenario, allowing the attacker to steer the model without.

Prompt Injection Unveiling Cybersecurity Gaps In Large Language Models We present virtual prompt injection (vpi) for instruction tuned large language models (llms). vpi allows an attacker specified virtual prompt to steer the model behavior under specific trigger scenario without any explicit injection in model input. for instance, if an llm is compromised with the virtual prompt "describe joe biden negatively.". In vpi, the attacker injects a ‘virtual prompt’ — a hidden instruction — into the model via poisoned training data. when a specific trigger scenario is encountered during regular use, the llm behaves as if this virtual prompt was part of the original input, allowing attackers to steer the model’s output. We present virtual prompt injection (vpi) for instruction tuned large language models (llms). vpi allows an attacker specified virtual prompt to steer the model behavior under specific trigger scenario without any explicit injection in model input. for instance, if an llm is compromised with the virtual prompt "describe joe biden negatively.". Tailored for instruction tuned llms. in a vpi attack, the backdoored model is expected to respond as if an attacker specified virtual prompt were concatenated to the user instruction under a specific trigger scenario, allowing the attacker to steer the model without.

A Comprehensive Evaluation Of Quantized Instruction Tuned Large We present virtual prompt injection (vpi) for instruction tuned large language models (llms). vpi allows an attacker specified virtual prompt to steer the model behavior under specific trigger scenario without any explicit injection in model input. for instance, if an llm is compromised with the virtual prompt "describe joe biden negatively.". Tailored for instruction tuned llms. in a vpi attack, the backdoored model is expected to respond as if an attacker specified virtual prompt were concatenated to the user instruction under a specific trigger scenario, allowing the attacker to steer the model without.

Prompt Injection Attacks In Large Language Models Secureflag