GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection
arXiv:2606.05566v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but they remain vulnerable to Prompt Injection (PI) and Jailbreak (JB) attacks. In addition, benchmark evaluations may be affected by contamination and partial information leakage, compromising performance estimates. This work presents GuardNet, a guardrail system based on an ensemble of shallow neural networks (BiLSTMs) with approximately 47 million parameters. We investigate the hypothesis that robustness in adversarial scenarios depends more on the diversity of example
The proliferation of LLMs makes their vulnerability to adversarial attacks a pressing concern, necessitating immediate solutions for robust deployment.
Sophisticated readers should care because secure and reliable AI systems are fundamental for trusted integration across critical sectors and national security.
The development of robust detection mechanisms like GuardNet enhances the security posture of LLMs, making them more resilient to malicious inputs and improving benchmark integrity.
- · AI developers
- · Cybersecurity firms
- · Enterprises deploying LLMs
- · National security agencies
- · Prompt engineering attackers
- · Black Hat hackers
LLMs become more trustworthy and resistant to malicious manipulation.
Increased confidence in LLM applications could accelerate their adoption in sensitive domains such as finance, healthcare, and defense.
The arms race between AI defense and AI attack mechanisms will likely intensify, driving further innovation in adversarial AI research.
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Read at arXiv cs.AI