Large language models (LLMs) like ChatGPT are increasingly powering AI agents designed to act autonomously on our behalf. While these agents offer exciting possibilities, from automating complex tasks to providing personalized assistance, new research reveals a concerning array of security, privacy, and ethical threats they pose. A recent survey from Zhejiang University dives deep into the vulnerabilities inherent in these LLM-powered agents, highlighting risks that range from malicious manipulation to unintended data leakage. Unlike standalone LLMs, agents interact with diverse information sources, including user inputs, external databases, and online tools. This interconnectedness creates numerous entry points for attackers. One major threat is 'goal hijacking,' where malicious actors manipulate an agent's objectives. Imagine a seemingly helpful chatbot subtly redirected to reveal your personal data or execute harmful commands. This can be achieved through cleverly crafted inputs or even by contaminating the external databases the agent relies on. Beyond external threats, LLM-based agents also suffer from internal flaws. 'Hallucinations,' where the agent generates incorrect or nonsensical information, become even more problematic when coupled with the agent's ability to act. These hallucinations can stem from biases in training data, incomplete learning, or even the inherent randomness of the LLM’s output generation process. Furthermore, the research explores vulnerabilities related to 'backdoor attacks', where malicious code is embedded within the agent, lying dormant until triggered by specific inputs. These attacks can be particularly insidious when targeting agent memory or tool interfaces. Privacy leakage is another major concern. Agents often access sensitive information, creating opportunities for accidental or malicious disclosure. The research emphasizes the risk of both training data leakage, where the LLM’s internal parameters inadvertently reveal sensitive training information, and contextual privacy leakage, where private data from user interactions or external databases is inadvertently exposed. The implications of these findings are far-reaching. As AI agents become more integrated into our lives, safeguarding them against these threats is paramount. Future research directions include developing more robust security mechanisms, designing AI architectures with inherent safety features, and establishing clear ethical guidelines and regulations for agent development and deployment. The journey toward truly trustworthy AI agents requires a collective effort, combining technical advancements with thoughtful policy-making to mitigate these hidden dangers.