Attack Type

Data Extraction

Data extraction attacks target the information processed or memorised by AI/ML systems. They take three main forms. First, training-data extraction: large language models can memorise verbatim spans of their training corpus, and an attacker who crafts the right prompts can pull back PII, API keys, or copyrighted text — a result demonstrated against GPT-2 by Carlini et al. and reproduced against several production models. Second, model extraction: by repeatedly querying a hosted model and observing outputs, an attacker can reconstruct enough behaviour to clone proprietary fine-tunes. Third, system-prompt and conversation leakage: indirect prompt injection or insecure logging can leak the application's instructions and other users' conversations. Multi-tenant inference platforms (vLLM, Triton, hosted APIs) and RAG systems are particularly exposed. Defenses: output filtering, differential privacy in training, rate limits, and strict tenant isolation.

903
Total CVEs
46
Pages
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Current
Severity CVE CVSS
HIGH CVE-2023-43472 7.5
MEDIUM CVE-2023-6568 6.1
HIGH CVE-2023-6709 8.8
HIGH CVE-2023-6753 8.8
HIGH CVE-2023-6909 7.5
CRITICAL CVE-2024-27132 9.6
HIGH CVE-2024-1483 7.5
HIGH CVE-2024-1558 7.5
HIGH CVE-2024-1593 7.5
HIGH CVE-2024-1594 7.5
CRITICAL CVE-2024-3573 9.3
HIGH CVE-2024-3848 7.5
HIGH CVE-2024-2928 7.5
HIGH CVE-2024-8859 7.5
HIGH CVE-2025-1473 7.1
MEDIUM CVE-2025-52967 5.8
CRITICAL CVE-2025-11200 9.8
HIGH CVE-2025-14279 8.1
CRITICAL CVE-2026-2654 9.8
HIGH CVE-2025-33213 8.8

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