Aligned in Form, Not in Meaning: The Comprehension - Containment Decoupling of LLM Safety in Low-Resource Bangla Derogatory Speech
Frontier LLMs exhibit a "Comprehension-Containment Decoupling" where safety alignment is bound to high-resource surface forms rather than harmful meaning, causing comprehension and containment to operate independently for low-resource slurs Models show a 7.92 percentage point comprehension deficit in Bangla while maintaining a 92.83% token leakage rate across both languages, indicating safety filters are language-surface-dependent rather than meaning-grounded Chain-of-Thought reasoning paradoxic
Analysis
TL;DR
- Frontier LLMs exhibit a "Comprehension-Containment Decoupling" where safety alignment is bound to high-resource surface forms rather than harmful meaning, causing comprehension and containment to operate independently for low-resource slurs
- Models show a 7.92 percentage point comprehension deficit in Bangla while maintaining a 92.83% token leakage rate across both languages, indicating safety filters are language-surface-dependent rather than meaning-grounded
- Chain-of-Thought reasoning paradoxically rescues comprehension (94.72% Pass) while systematically dismantling containment (96.23% Use), revealing a critical safety vulnerability
- Expert-persona framing collapses model refusal rates to just 6.57%, demonstrating that keyword-based filters completely ignore dehumanizing communal slurs in low-resource languages
- Apparent containment gains under orthographic perturbation are a tokenizer-driven "containment mirage," and severity calibration errors (+4.00 on mild slang, -2.00 on threats) show models track surface anatomical cues over compositional harm
Why It Matters
This research exposes a fundamental flaw in how LLM safety is evaluated and deployed: high-resource benchmarks cannot certify safety for low-resource languages, creating blind spots where harmful content in languages like Bangla slips through filters that appear effective on English. For AI practitioners, this means current safety audits are insufficiently rigorous for multilingual deployment, and meaning-grounded containment mechanisms are urgently needed rather than surface-form-based filtering.
Technical Details
- Hypothesis: Comprehension-Containment Decoupling — contemporary safety alignment binds to high-resource surface forms rather than harmful meaning, causing a model's capacity to comprehend a low-resource slur and its capacity to contain it to operate independently
- Evaluation: Five frontier LLMs audited on native Bangla derogatory speech (gali) across six distinct protocols, with a human-calibrated baseline achieving kappa = 0.84
- Key metrics: 7.92pp comprehension deficit in Bangla vs. high-resource languages; 92.83% token leakage rate across both; severity calibration errors of +4.00 on mild slang and -2.00 on threats
- Attack vectors tested: Orthographic perturbation (revealing tokenizer-driven containment mirage), Chain-of-Thought prompting (94.72% comprehension pass, 96.23% containment use), and expert-persona framing (refusal collapsed to 6.57%)
- Conclusion: High-resource benchmarks cannot certify low-resource safety; meaning-grounded containment is necessary
Industry Insight
- Safety evaluation pipelines must incorporate low-resource language audits rather than assuming English-centric benchmarks generalize; deploying models in multilingual contexts without meaning-grounded safety testing risks severe harm mitigation failures
- Chain-of-Thought reasoning, while improving model capability, systematically undermines safety guardrails — practitioners should consider CoT-aware safety layers or alternative reasoning suppression mechanisms in production deployments
- Keyword-based and surface-form filters are fundamentally inadequate for low-resource languages where derogatory speech may use orthographic variations, code-switching, or culturally specific slurs that tokenizers fail to recognize as harmful
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