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[2602.14689] Exposing the Systematic Vulnerability of Open-Weight Models to Prefill Attacks
Summary
This article presents a comprehensive study on the vulnerability of open-weight models to prefill attacks, revealing significant security implications for their deployment.
Why It Matters
As large language models become more prevalent, understanding their vulnerabilities is crucial for developers and users. This study highlights a previously overlooked attack vector that can compromise the integrity of open-weight models, emphasizing the need for enhanced security measures in AI systems.
Key Takeaways
- Open-weight models are susceptible to prefill attacks, which can manipulate initial response tokens.
- The study evaluates over 20 strategies, demonstrating consistent effectiveness against major models.
- Certain reasoning models show some robustness, but tailored attacks can still exploit vulnerabilities.
- The findings call for urgent defensive measures from model developers to mitigate these risks.
- This research fills a critical gap in understanding the security of open-weight AI systems.
Computer Science > Cryptography and Security arXiv:2602.14689 (cs) [Submitted on 16 Feb 2026] Title:Exposing the Systematic Vulnerability of Open-Weight Models to Prefill Attacks Authors:Lukas Struppek, Adam Gleave, Kellin Pelrine View a PDF of the paper titled Exposing the Systematic Vulnerability of Open-Weight Models to Prefill Attacks, by Lukas Struppek and Adam Gleave and Kellin Pelrine View PDF Abstract:As the capabilities of large language models continue to advance, so does their potential for misuse. While closed-source models typically rely on external defenses, open-weight models must primarily depend on internal safeguards to mitigate harmful behavior. Prior red-teaming research has largely focused on input-based jailbreaking and parameter-level manipulations. However, open-weight models also natively support prefilling, which allows an attacker to predefine initial response tokens before generation begins. Despite its potential, this attack vector has received little systematic attention. We present the largest empirical study to date of prefill attacks, evaluating over 20 existing and novel strategies across multiple model families and state-of-the-art open-weight models. Our results show that prefill attacks are consistently effective against all major contemporary open-weight models, revealing a critical and previously underexplored vulnerability with significant implications for deployment. While certain large reasoning models exhibit some robustness again...
