Large language models (LLMs) like ChatGPT are impressive, but they have a hidden weakness: they can be tricked into generating incredibly long, often nonsensical responses. Researchers have discovered a new type of attack, dubbed "Engorgio," that exploits this vulnerability. By crafting special prompts, attackers can make LLMs "babble" endlessly, reaching the maximum output length and significantly increasing the computational cost of running the model. This attack raises serious concerns about the availability and security of LLM services. Imagine a scenario where a malicious user floods a cloud-based LLM service with these "Engorgio" prompts. The server gets bogged down processing these endless responses, slowing down or even blocking access for legitimate users. The research delves into the technical details of how these adversarial prompts work. LLMs generate text by predicting the next token in a sequence until they hit an "end-of-sentence" token (). The "Engorgio" attack crafts prompts that make the LLM less likely to predict this token, causing it to continue generating text far beyond what a normal prompt would trigger. The researchers tested "Engorgio" on a range of LLMs, from smaller open-source models to large, fine-tuned models like Alpaca and Vicuna. The results were striking: the attack consistently led to outputs reaching nearly 100% of the maximum allowed length, sometimes up to 13 times longer than normal. This drastic increase in output length directly translates to higher computational costs and slower response times. While this research highlights a significant vulnerability, it also offers valuable insights for improving LLMs. By studying how these adversarial prompts work, developers can learn to make models more robust and efficient, recognizing when to stop generating text instead of babbling on unnecessarily. Furthermore, "Engorgio" could be used as a tool for stress-testing LLM services, helping providers understand the limits of their systems and optimize performance under heavy load. The "Engorgio" attack poses a real threat to the stability and security of LLM services. As LLMs become more prevalent in various applications, protecting them from such attacks becomes crucial. This research is a wake-up call, urging developers and service providers to address this vulnerability and build more resilient AI systems.
🍰 Interesting in building your own agents?
PromptLayer provides the tools to manage and monitor prompts with your whole team. Get started for free.
Question & Answers
How does the 'Engorgio' attack technically manipulate LLMs to generate endless text?
The Engorgio attack works by manipulating the LLM's token prediction mechanism. At its core, LLMs generate text by predicting the next token in a sequence until they encounter an end-of-sentence (EOS) token. The attack crafts special prompts that minimize the probability of the model predicting the EOS token, causing it to continue generating text indefinitely. This manipulation can result in outputs up to 13 times longer than normal responses, significantly increasing computational load. For example, an attacker could craft a prompt that makes the model continuously generate descriptions or explanations without reaching a natural conclusion, similar to a person stuck in an infinite loop of thought.
What are the main security risks of AI language models in business applications?
AI language models pose several security risks in business settings, particularly regarding availability and resource manipulation. As demonstrated by the Engorgio attack, malicious users can overload systems and create denial-of-service situations by forcing models to generate excessive outputs. This can lead to increased operational costs, reduced service availability for legitimate users, and potential system crashes. For businesses, this means higher infrastructure costs, damaged customer experience, and potential revenue loss. Common applications like customer service chatbots or content generation tools could become unreliable or unavailable when targeted by such attacks.
What are the benefits of stress-testing AI systems before deployment?
Stress-testing AI systems offers crucial advantages for ensuring robust and reliable service delivery. It helps identify performance limits, security vulnerabilities, and potential failure points before they impact real users. By conducting thorough stress tests, organizations can optimize their infrastructure, develop better scaling strategies, and implement protective measures against potential attacks. For example, techniques like the Engorgio attack can be used constructively to understand system limitations and improve resilience. This proactive approach helps businesses maintain service quality, protect against costly downtimes, and build user trust in their AI-powered solutions.
PromptLayer Features
Testing & Evaluation
Enables systematic testing of LLM responses for vulnerability to length-based attacks and monitoring of output patterns
Implementation Details
Set up batch tests with varying prompt lengths, implement length monitoring metrics, create regression tests for output size consistency
Key Benefits
• Early detection of vulnerable prompt patterns
• Automated monitoring of response lengths
• Systematic validation of security measures
.png){kind=icon}
.svg)
.svg)
.svg)
.svg)
