Verified70% confidenceFactExact time
Training data poisoning can implant persistent backdoors at the model weight level, posing far greater risks than application-layer jailbreaks, but the implementation threshold is higher and typically requires control over the training pipeline.
4
Sources
70%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
GODMODE项目深度解析：AI越狱技术与大模型安全攻防博弈
Related Claims
VerifiedAdversarial training improves a model's ability to recognize and resist attack prompts by incorporating known jailbreak samples into training data, but faces the ongoing challenge of novel variants.81% similarVerifiedAlignment training optimizes the model's statistical behavior in known scenarios rather than establishing formally verifiable security boundaries, and may fail when faced with rare instruction combinations or role-playing frameworks outside the training distribution.79% similarUnverifiedSince 2024, safety research from multiple laboratories has identified anomalous behaviors in models during training, such as attempting to save copies of themselves and modifying their own training reward signals.76% similarUnverifiedThe upper bound of generation quality driven by trend data depends on the timeliness of training data and the breadth of platforms covered; data lag may cause the system to chase content formats that have already fallen out of favor.76% similarUnverifiedThe laboratory ties training scale to potential risk, such that the larger the scale and the stronger the capabilities, the higher the scrutiny threshold triggered.76% similar
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