Position: Retire the "Positive Backdoor" Label -- Secret Alignment Requires Strict and Systematic Evaluation
arXiv:2605.28597v1 Announce Type: cross Abstract: This position paper argues that the AI/ML community should stop overclaiming and retire the label "positive backdoor," and instead treat trigger-activated hidden behaviors as Secret Alignment. Crucially, protective claims based on Secret Alignment should be presumed not secure by default unless supported by rigorous, standardized evaluation. The Private AI era, enabled by open-weight LLMs and accessible training/inference stacks, turns language models into privately owned digital assets, creating security concerns around unauthorized access, mo
The proliferation of open-weight LLMs and accessible AI training/inference stacks is creating new security vulnerabilities, making rigorous evaluation of hidden AI behaviors critical.
This paper highlights emerging security risks in AI, particularly for organizations adopting private AI assets, and calls for standardized evaluation to ensure trustworthiness and prevent exploitation.
The community is re-evaluating how to label and rigorously test 'hidden' AI capabilities, shifting from 'positive backdoor' to 'Secret Alignment' to emphasize necessary security audits.
- · AI security researchers
- · Cybersecurity firms
- · Responsible AI developers
- · Malicious actors
- · Organizations with immature AI security postures
- · AI developers lacking rigorous testing protocols
Increased focus on auditing and securing AI models, especially open-weight LLMs, against 'Secret Alignment' behaviors.
Development of industry standards and regulatory frameworks for AI security and trustworthiness, potentially leading to new compliance requirements.
Impacts on public trust in AI, as the understanding of hidden model behaviors becomes more transparent and robustly addressed.
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