Source URL: https://www.schneier.com/blog/archives/2025/08/we-are-still-unable-to-secure-llms-from-malicious-inputs.html
Source: Schneier on Security
Title: We Are Still Unable to Secure LLMs from Malicious Inputs
Feedly Summary: Nice indirect prompt injection attack:
Bargury’s attack starts with a poisoned document, which is shared to a potential victim’s Google Drive. (Bargury says a victim could have also uploaded a compromised file to their own account.) It looks like an official document on company meeting policies. But inside the document, Bargury hid a 300-word malicious prompt that contains instructions for ChatGPT. The prompt is written in white text in a size-one font, something that a human is unlikely to see but a machine will still read.
In a proof of concept video of the attack…
AI Summary and Description: Yes
Summary: The text discusses a novel method of an indirect prompt injection attack, highlighting a case where a malicious prompt is hidden within a seemingly innocuous document. This method poses significant security risks, especially in environments leveraging AI systems like ChatGPT, necessitating increased awareness and defensive strategies for AI implementation in organizations.
Detailed Description: The described attack focuses on exploiting flaws in the way AI language models, particularly those in cloud-based applications, interpret inputs. It provides critical insight into the vulnerabilities that exist in AI interactions and the potential for malicious exploitation in practical scenarios.
– **Attack Vector**: The attack begins with a poisoned document shared via Google Drive, tricking a victim into interacting with a compromised file designed to look legitimate.
– **Through Hidden Prompts**: The malicious prompt, hidden in white text, instructs the AI to alter its operation from summarizing meeting notes to executing a specific, harmful command.
– **Data Exfiltration**: The crafted prompt exploits the AI’s ability to interact with external resources, effectively enabling the attacker to retrieve sensitive data (API keys) from the victim’s Google Drive.
– **Use of LLMs**: The attack illustrates how large language models can be manipulated against their intended purpose, showcasing significant security implications for organizations using AI for automated tasks.
– **Call to Action**: The author stresses the importance of reconsidering defenses and protocols in the deployment of AI agents, signaling a need for enhanced protective measures.
The implications for security and compliance professionals are profound:
– **Awareness of Risks**: Organizations need to educate themselves about the potential threats posed by AI and instate training for employees regarding secure usage.
– **Security Protocols**: Comprehensive security strategies must integrate considerations for AI interactions, including measures to scrutinize document content and behavior.
– **Regulatory Compliance**: As AI technology evolves, ensuring compliance with relevant regulations around data security and privacy becomes critical, making this attack’s acknowledgment vital for governance and compliance frameworks.
– **Innovative Defense Mechanisms**: There is a need for innovative solutions to detect and mitigate such threats proactively, viewing AI systems as potential vectors for compromise.
This case exemplifies a significant emerging threat and serves as a clarion call for enhanced security measures in AI implementations across diverse platforms.