arXiv:2601.14340v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely integrated into interactive systems such as dialogue agents and task-oriented assistants. This growing ecosystem also raises supply-chain risks, where adversaries can distribute poisoned models that degrade downstream reliability and user trust. Existing backdoor attacks and defenses are largely prompt-centric, focusing on user-visible triggers while overlooking structural signals in multi-turn conversations. We propose Turn-based Structural Trigger (TST), a backdoor attack that activates from dia
The increasing integration of LLMs into interactive systems necessitates new security research into vulnerabilities beyond traditional prompt-based attacks.
This research reveals a critical new vector for 'backdoor' attacks in AI supply chains, compromising model reliability and user trust at a foundational level.
The focus of AI security now expands beyond prompt-centric vulnerabilities to include structural signals within multi-turn conversations, complicating existing defense strategies.
- · AI security researchers
- · Cybersecurity companies specializing in AI audits
- · Developers of robust LLM security frameworks
- · LLM providers with insecure supply chains
- · Organizations deploying unverified LLMs
- · Users of compromised interactive AI systems
Identification of a novel, harder-to-detect backdoor attack vector in multi-turn LLMs.
Increased scrutiny and demand for supply chain security in AI models, leading to new verification protocols.
Potential for government regulation around AI model provenance and security standards to prevent widespread malicious use.
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Read at arXiv cs.LG