生成aiのセキュリティ概観 ――リスク・攻撃手法・対策――

Q: What does this research mean for the field?

Generative AI models face significant security vulnerabilities, such as being susceptible to targeted malfunctions triggered by specific keywords using as few as 250 malicious data samples. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Yoshimasa Iwase

doi:10.1587/bplus.20.7

January 1, 2026Denshi Jouhou Tsuushin Gakkai Tsuushin Sosaieti magajin/B-plus/Denshi Jouhou Tsuushin Gakkai Tsuushin Sosaeti magajinOpen Access

生成aiのセキュリティ概観 ――リスク・攻撃手法・対策――

View Full Paper

Ask AI

Bookmark

Share

Key Points

The aim is to evaluate security risks and vulnerabilities in generative AI systems.
Analysis of potential triggering conditions for AI errors using minimal adversarial examples.
Review of known attack methods against generative AI.
Assessment of countermeasures to mitigate security breaches.
Demonstrates that around 250 instances of malicious data can trigger faults in AI with specific keywords.
Identifies various attack methods effective against current generative AI implementations.

No abstract yet

We only have this paper's title and publication details.

Read it at the source

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

Overview identifies risks and attack methods in generative AI systems, suggesting necessary countermeasures.

Abstract

究 (4) では，僅か 250 件程度の不正データがあれば，特定のキーワードに反応して誤作動させることが可能だとされている．

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Yoshimasa Iwase (2026) studied this question.

synapsesocial.com/papers/6a1fc3a6dee9eb8c0dce51c7 https://doi.org/10.1587/bplus.20.7

View Full Paper

Ask AI

Bookmark

Share

生成aiのセキュリティ概観 ――リスク・攻撃手法・対策――

Key Points

No abstract yet

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

生成aiのセキュリティ概観 ――リスク・攻撃手法・対策――

Key Points

No abstract yet

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading