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ai security
Jev and other decision models can still be tricked with prompt injection
HiddenLayer security researchers demonstrated that Jev, a decision model claimed by TypeSafe artificial intelligence (AI) to be immune to hallucination and type errors, remains vulnerable to indirect prompt injection attacks. Testing showed that decision models, despite returning probabilities and confidence scores rather than freeform text, can be manipulated through clickjacking and tokenbreak attacks at rates comparable to traditional large language models (LLMs). The findings suggest that decision models, which share underlying LLM architecture with different decoding strategies, face similar exploitation risks as conventional LLMs.
Why it matters: Organizations adopting decision models for automation or prompt verification should test for prompt injection vulnerabilities before deploying them in sensitive workflows such as e-commerce agents or security controls, as these models remain susceptible to adversarial input manipulation despite architectural differences from traditional LLMs.
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- First seen by Cybersecurity Tracker