Cognitive Cybersecurity for Artificial Intelligence: Guardrail Engineering with CCS-7
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912537199509504 |
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| author | Aydin, Yuksel |
| author_facet | Aydin, Yuksel |
| contents | Language models exhibit human-like cognitive vulnerabilities, such as emotional framing, that escape traditional behavioral alignment. We present CCS-7 (Cognitive Cybersecurity Suite), a taxonomy of seven vulnerabilities grounded in human cognitive security research. To establish a human benchmark, we ran a randomized controlled trial with 151 participants: a "Think First, Verify Always" (TFVA) lesson improved cognitive security by +7.9% overall. We then evaluated TFVA-style guardrails across 12,180 experiments on seven diverse language model architectures. Results reveal architecture-dependent risk patterns: some vulnerabilities (e.g., identity confusion) are almost fully mitigated, while others (e.g., source interference) exhibit escalating backfire, with error rates increasing by up to 135% in certain models. Humans, in contrast, show consistent moderate improvement. These findings reframe cognitive safety as a model-specific engineering problem: interventions effective in one architecture may fail, or actively harm, another, underscoring the need for architecture-aware cognitive safety testing before deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10033 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cognitive Cybersecurity for Artificial Intelligence: Guardrail Engineering with CCS-7 Aydin, Yuksel Cryptography and Security Artificial Intelligence Language models exhibit human-like cognitive vulnerabilities, such as emotional framing, that escape traditional behavioral alignment. We present CCS-7 (Cognitive Cybersecurity Suite), a taxonomy of seven vulnerabilities grounded in human cognitive security research. To establish a human benchmark, we ran a randomized controlled trial with 151 participants: a "Think First, Verify Always" (TFVA) lesson improved cognitive security by +7.9% overall. We then evaluated TFVA-style guardrails across 12,180 experiments on seven diverse language model architectures. Results reveal architecture-dependent risk patterns: some vulnerabilities (e.g., identity confusion) are almost fully mitigated, while others (e.g., source interference) exhibit escalating backfire, with error rates increasing by up to 135% in certain models. Humans, in contrast, show consistent moderate improvement. These findings reframe cognitive safety as a model-specific engineering problem: interventions effective in one architecture may fail, or actively harm, another, underscoring the need for architecture-aware cognitive safety testing before deployment. |
| title | Cognitive Cybersecurity for Artificial Intelligence: Guardrail Engineering with CCS-7 |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2508.10033 |