arXiv:2606.06833v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transcription decisions are inherently made on incomplete information. This causal constraint serves as an information bottleneck on attackers, significantly limiting attack performance. Our new Semantic Gambit attack breaks this causal limitation by augmenting the adversary with predictive context derived from a Large Language Model in real-time. Our experiments show that this form of augmentation can el
The increasing sophistication and real-time application of Large Language Models (LLMs) enable new forms of adversarial attacks that leverage predictive context, challenging existing security paradigms for AI systems.
This development highlights a critical and evolving vulnerability in real-time AI systems, particularly Automatic Speech Recognition (ASR), demonstrating how advanced AI can be used to bypass current security measures and emphasizing the need for more robust, context-aware defenses.
The ability of attackers to use real-time predictive context from LLMs to overcome causal limitations in ASR systems means that traditional defenses based on temporal constraints are increasingly insufficient, necessitating a re-evaluation of security strategies for AI.
- · AI security researchers
- · Cybersecurity firms specializing in AI red-teaming
- · Developers of new AI defense mechanisms
- · Vendors of existing ASR systems lacking advanced defenses
- · Organizations relying on real-time ASR for critical functions
- · Users vulnerable to sophisticated voice-based attacks
Adversarial attacks on ASR systems will become more effective and harder to detect in real-time.
Increased investment in AI defense mechanisms will be required, potentially leading to new security standards for real-time AI applications.
The development of 'AI versus AI' defense and offense mechanisms could create a continuous arms race in the realm of AI security, perpetually challenging system integrity.
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Read at arXiv cs.LG