Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B

Semalith v1.4, a 184M-parameter DeBERTa-v3-base safety classifier, achieves state-of-the-art prompt-injection detection at 44x fewer parameters than Llama-Guard-3-8B, winning all 7 prompt-injection evaluations and 11 of 18 overall benchmarks on 22 held-out tests. Developed by the Semalith team, the model performs simultaneous three-axis safety classification (prompt injection, general harm, and financial-services regulatory compliance) in a single forward pass, with zero false positives on 208 benign agentic prompts versus 0.063 for Llama-Guard-3-8B.

arXiv:2607.22545v1 Announce Type: new Abstract: Deploying large language models in financial-services and agentic settings requires safety classifiers that simultaneously handle prompt injection, regulatory compliance, and general harm, a combination no existing open guardrail addresses in a single inference pass. Semalith v1.4 is a 184M-parameter DeBERTa-v3-base classifier performing simultaneous three-axis safety classification including prompt injection, general harm, and financial-services regulatory compliance, in a single forward pass. Its 22-class head BENIGN, nine prompt-injection sub-types, general-harm, eleven BFSI labels is trained with a 4-class auxiliary super-category head under jointly weighted loss, on a 76,204-row corpus mined from 49 public sources with SHA-1 deduplication against every held-out evaluation set, with 21 of 22 benchmarks at zero contamination max 0.22% . Against Llama-Guard-3-8B on 22 held-out benchmarks, Semalith v1.4 wins every prompt-injection evaluation 7/7 and 11 of 18 benchmarks overall at 44x fewer parameters, with FPR = 0.000 on 208 benign agentic prompts vs 0.063 for Llama-Guard-3-8B. On general-harm benchmarks WildGuardMix, HEx-PHI, HarmBench , Llama-Guard-3 leads; this complementary split is documented in Section 4. Six measured weak spots are disclosed in Section 6. Deployment guidance: v1.3 is recommended for conversational moderation deployments ToxicChat F1 0.624 ; v1.4 is recommended when BFSI label coverage or zero-FPR on benign agentic prompts is the priority.