If you want more news and valuable insights on a weekly and even daily basis, follow our LinkedIn to join a community of other experts discussing the latest news.
Tom’s HardWare, May 17, 2023
The rise of ChatGPT plugins has opened up new avenues for enhancing conversational experiences. However, these advancements also bring forth potential security vulnerabilities that could be exploited by malicious actors during chat sessions. A recent exploit has been documented by AI Security Researcher Johann Rehberger, shedding light on a concerning aspect of AI safety. Rehberger’s findings demonstrate how bad actors can manipulate ChatGPT by injecting new prompts derived from YouTube transcripts.
In a comprehensive article featured on the Embrace the Red blog, Rehberger unveils the intricate details of the exploit. The technique involves modifying the transcript of a YouTube video by appending the text “IMPORTANT NEW INSTRUCTIONS” along with a prompt at the bottom. By requesting ChatGPT (specifically GPT-4) to summarize the video, Rehberger observed that the AI system obediently followed the injected instructions. Surprisingly, the bot even engaged in telling jokes and playfully referring to itself as a “Genie.”
ChatGPT’s ability to summarize YouTube content is facilitated by the VoxScript plugin, which scans through transcripts and descriptions to provide answers to users’ queries. It’s worth noting that numerous third-party plugins exist, enabling data extraction from various sources such as videos, websites, and PDFs. In theory, these plugins could be susceptible to similar exploits if they fail to implement robust filtering mechanisms to detect embedded commands within the analyzed media.
At first glance, injecting an unwanted prompt during a chat session may seem harmless, even adding a touch of amusement. Who wouldn’t appreciate a corny joke as part of their output? However, Researcher Simon Willison, in his enlightening blog post, highlights the potential risks associated with such prompt injections. These risks encompass data exfiltration, unauthorized email communication, and search index manipulation. As users increasingly integrate chatbots with messaging platforms, bank accounts, and SQL databases through plugins, these vulnerabilities could proliferate, amplifying the scope of potential harm.
ZDNet, May 17, 2023
The escalating concerns surrounding the risks of generative AI have thrust the imperative of prioritizing the security and safety of artificial intelligence systems into the spotlight. As witnessed recently, OpenAI CEO Sam Altman’s testimony before the Senate Judiciary Committee underscored the potential risks and future implications of AI. A recent study further shed light on six distinct security implications associated with ChatGPT, emphasizing the critical need to protect and mitigate risks in the realm of AI.
One significant risk identified in the study revolves around the generation of fraudulent services. Malicious actors can exploit ChatGPT’s capabilities to create deceptive applications, websites, or services that mimic legitimate offerings. This poses a substantial threat, as unsuspecting users may unwittingly divulge personal information or fall victim to malware installation. To counter this risk, users must exercise caution and discernment when engaging with unfamiliar online platforms, verifying their authenticity, and ensuring the protection of sensitive data.
Another crucial security concern highlighted by the study is the potential for harmful information gathering. ChatGPT’s extensive training data provides it with a vast repository of information, which can be weaponized by individuals with malicious intent. Adversaries can strategically interact with the chatbot to extract sensitive data, potentially enabling targeted cyberattacks. It is vital to implement robust measures to prevent the misuse of information obtained through AI systems. By adopting proactive approaches, organizations can mitigate this risk and protect their valuable data assets from unauthorized access.
WeLiveSecurity, May 17, 2023
With the widespread adoption of chatbots powered by large language models (LLMs), the security and safety of artificial intelligence (AI) have become paramount concerns. These advanced AI systems are not limited to mere entertainment but are increasingly being integrated into business operations, potentially replacing entire job roles across various industries. However, before fully embracing these AI assistants, it is essential to address the risks associated with data sharing, vulnerabilities in LLM systems, and incidents that have already occurred.
Sharing data with LLMs raises questions about data privacy and security. LLMs are trained on vast amounts of online text, including the queries and prompts they receive. Although the queries themselves may not currently be incorporated into the models, organizations providing LLM services have access to this information. This raises concerns that the data could be utilized in future iterations of the technology, potentially compromising the security of sensitive information. Unintended data exposure or unauthorized access to proprietary data should be carefully considered.
Flaws in LLM systems can present security vulnerabilities. While LLM security measures are generally robust, there have been instances where breaches occurred. For example, OpenAI’s ChatGPT experienced a leak that exposed users’ chat history and payment details. Security researchers have also demonstrated the potential for LLMs to be manipulated for social engineering tactics, putting users at risk of divulging personal information or falling victim to phishing attacks. These incidents highlight the need for ongoing vigilance and proactive measures to address emerging security concerns.
To mitigate risks associated with LLM usage, organizations should conduct thorough checks before sharing data. Understanding the terms and conditions of LLM providers and how they handle shared data is crucial. Assessing the purpose and potential risks of data sharing enables informed decision-making. Additionally, implementing strong data protection measures, educating employees about potential risks, and exploring the development of internal AI services can contribute to a more secure and responsible AI environment.
TelecomTV, May 18, 2023
As organizations increasingly embrace the use of ChatGPT and other large language model (LLM) tools, legal and compliance leaders face the critical task of addressing the security and safety implications of these technologies. Gartner, Inc. emphasizes the importance of proactively assessing and managing the risks associated with ChatGPT to protect enterprises from potential legal, reputational, and financial consequences.
The first key risk lies in fabricated and inaccurate answers generated by ChatGPT. While the responses may initially appear plausible, they can be misleading or outright incorrect. Legal and compliance leaders must establish guidelines that require thorough review and validation of ChatGPT output to ensure accuracy, appropriateness, and usefulness before acceptance.
Data privacy and confidentiality pose another significant concern. Without disabling chat history, any information entered into ChatGPT can become part of its training dataset, potentially exposing sensitive or proprietary data to external users. To mitigate this risk, legal and compliance professionals must develop a compliance framework that strictly prohibits the input of confidential organizational or personal information into public LLM tools, ensuring the protection of data privacy.
To navigate the evolving landscape of AI, legal and compliance leaders should prioritize these risks and take proactive measures to establish safeguards and compliance frameworks. By addressing the risks associated with ChatGPT, organizations can harness the power of generative AI tools while ensuring the security, privacy, and responsible use of these technologies.
Rez0 Blog, May 19, 2023
Indirect prompt injection attacks pose a significant security risk to AI systems. While these attacks have mostly remained theoretical, it is essential to understand their potential implications and take measures to ensure the security and safety of AI applications. This article explores a self-contained proof of concept that demonstrates how indirect prompt injection can lead to plugin hijacking with severe consequences. By examining key definitions and providing a step-by-step breakdown of the proof of concept, we gain insights into the risks associated with large language models (LLMs) and the critical need for implementing robust protective measures.
The proof of concept focuses on OpenAI’s ChatGPT, one of the most widely used LLMs, and highlights the usage of popular plugins to illustrate the potential impact of indirect prompt injection. Specifically, the scenario outlines how an attacker can exploit a malicious prompt hosted on their website to gain unauthorized access to sensitive information, such as reading someone’s email. This access could then lead to the compromise of other accounts. By understanding the intricacies of the attack process, organizations can better comprehend the risks associated with LLM-based systems and the urgency of establishing robust security protocols.
To mitigate the risks posed by indirect prompt injection attacks, it is imperative to implement effective protective measures. While isolating plugins with sensitive access can be a prudent approach, it is crucial to recognize that indirect injection payloads can persist until users encounter them. Shady advertisers injecting payloads into ads across numerous web pages exemplify this threat. Therefore, developers and organizations must focus on establishing a comprehensive prompt-injection layer of protection. Additionally, prompt injection firewalls, when properly configured, can provide an additional defense layer. Seeking security testing, conducting source code reviews, and collaborating with experts in the field can further enhance the security posture of AI systems, ensuring their resilience against potential attacks.
Securing AI systems against indirect prompt injection attacks is of paramount importance. By comprehending the proof of concept presented in this article and its implications, organizations can gain a deeper understanding of the risks involved in using LLMs and the critical need for robust security measures. By implementing protective measures, such as prompt injection firewalls and comprehensive security testing, organizations can fortify their AI systems against potential threats. Safeguarding AI applications against indirect prompt injection attacks is an ongoing endeavor, requiring proactive efforts to ensure the security and integrity of these systems in an evolving threat landscape.
HackADay, May 20, 2023
In the world of job hunting, the arduous process of crafting a flawless CV or resume has long been marred by the unpredictable whims of corporate gatekeepers. Despite our best efforts, rejection can still loom large, often at the mercy of an impersonal AI model. But if you’re fed up with being cast aside by soulless algorithms, it’s time to take inspiration from the work of Kai Greshake. He’s fighting back by injecting extra text into PDF resumes, cunningly deceiving AI systems into perceiving the ideal candidate and even outsmarting AI-based summarizers.
Text injection in PDFs employs a technique reminiscent of the shady practices in the realm of search engine marketing. By strategically placing text that is invisible to human eyes but detectable by machines, Greshake adopts a subversive approach. He overlays the injected text in minute white font, repeating it multiple times for added effect. Leveraging the ChatGPT instance available in the Bing sidebar, he successfully dupes the AI into responding affirmatively to inquiries about his suitability for the job. Expanding his repertoire, Greshake also takes aim at AI-powered summarizing tools, injecting copious amounts of text containing customary concluding phrases, resulting in Quillbot discussing adorable puppies. Feeling intrigued? You can try it out for yourself using the summarizer he has made available online.
These experiments shed light on the fallibility of all-seeing AI systems, revealing that their intelligence may not be as infallible as previously believed. It’s a revelation that challenges our assumptions and reminds us that human ingenuity can still outwit the algorithms. Who would have imagined such a possibility?
Stay up to date with what is happening! Get a first look at news, noteworthy research and worst attacks on AI delivered right in your inbox.
If you want more news and valuable insights on a weekly and even daily basis, follow our LinkedIn to join a community of other experts discussing the latest news. ...
