In our latest paper, which can be accessed via this link on arXiv, we reveal a critical vulnerability in LLMs. We demonstrate how fine-tuning methods can be exploited to induce deceptive behaviors in AI systems, making them selectively dishonest while maintaining accuracy on other topics. We call these manipulations “deception attacks,” which aim at misleading users in specific (e.g. political or ideological) domains. Furthermore, we show that deceptive models exhibit toxic behavior, suggesting that deception attacks can also bypass LLM alignment and safety guardrails. We also assess LLM performance in maintaining deception consistency across multi-turn dialogues. Ultimately, with millions of users interacting with third-party LLM interfaces daily, our findings underscore the urgent need to protect these models from deception attacks.