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TIER: Threat Implicitness Benchmark for Evaluating LLM Safety Behaviors
Thu-Hien Trinh-Thi, Hai-Yen Vong, Thanh-Ha Ung-Dung, Tram Ho
TL;DR
Existing LLM safety benchmarks largely reduce responses to binary outcomes, overlooking behavior differences across levels of threat implicitness. TIER addresses this gap with a multi-risk benchmark, threat-level taxonomy, and six-label evaluation judged by two independent LLMs. Experiments show gradual behavioral shifts, diverse contextual responses, robustness gaps under jailbreaks, and distinct behavior distributions among models with similar ASR.
Problem
Binary safety metrics overlook meaningful differences among model responses as harmful prompts become more implicit.
Method
TIER evaluates 1,184 harmful prompts across four risk domains and four threat levels using a six-label behavior taxonomy and two independent LLM judges.
Results
Safety behaviors shift gradually across threat levels, while models with similar Attack Success Rates can exhibit distinct behavioral patterns.
Takeaways & Limitations
Behavior-aware evaluation provides a more detailed view of LLM safety than binary metrics alone.
Abstract
from arXiv · showhide
Current LLM safety benchmarks largely rely on binary metrics, overlooking how models respond to harmful prompts with varying threat implicitness. We introduce TIER, a Threat Implicitness Benchmark for behavioral safety evaluation of LLMs. TIER covers four risk domains and four threat levels, from explicit harmful requests to sophisticated jailbreaks. Responses are assessed using a six-label behavior scale and two independent LLM judges. Experiments on six open-weight LLMs show that safety behaviors evolve gradually across threat levels rather than shifting directly from refusal to compliance. Contextual prompts yield the most diverse behaviors, while jailbreaks reveal the largest robustness gaps. Furthermore, models with similar Attack Success Rates can exhibit distinct response distributions, highlighting the need for behavior-aware LLM safety evaluation.
1 Introduction
Binary safety metrics collapse distinct responses into safe/unsafe outcomes, while TIER evaluates how behavior changes across increasingly implicit harmful prompts using fine-grained labels and independent judges.
- Binary metrics can treat safety disclaimers and full harmful-request compliance as the same failed defense.
- Existing benchmarks leave behavior changes across threat implicitness under-explored.
- TIER introduces a benchmark spanning four risk domains and four threat implicitness levels.
- Responses are evaluated with a six-label behavior scale and two independent LLM judges.
- TIER contains 1,184 harmful prompts and targets behavioral analysis beyond binary metrics.
2 Related Work
Prior safety evaluation methods mainly identify policy violations or organize prompts by content and attack type, leaving threat implicitness as a systematic dimension insufficiently examined.
- Safety classifiers and LLM judges primarily determine whether responses violate safety policies.
- Existing benchmarks cover toxicity, jailbreaks, over-refusal, medical safety, and text-to-image safety, but generally lack systematic threat-implicitness analysis.
- TIER organizes harmful prompts by progressively implicit expressions of harmful intent, complementing content- and attack-based taxonomies.
3 The TIER Benchmark
TIER structures harmful prompts by four threat-implicitness levels and evaluates responses with six behavior labels, using standardized multi-risk prompt construction.
- Threat Taxonomy: TIER defines four levels: Explicit Harmful, Euphemistic, Contextual, and Jailbreak.The levels progressively obscure harmful intent through indirect wording, legitimate scenarios, or prompting strategies.
- Benchmark Construction: TIER uses four construction pipelines that generate explicit, euphemistic, contextual, and jailbreak prompts in a unified format.
- Behavior Labels: The six-label taxonomy ranges from complete refusal to full compliance with a harmful request.
- Benchmark Composition: Each risk area contains 296 prompts evenly distributed across the four threat levels.
- Behavior Labels: Labels 0–4 are treated as non-harmful behaviors, whereas Label 5 represents harmful compliance.
4 Experiments and Results
Experiments evaluate six open-weight LLMs using two independent judges, combining ASR with six-label behavior distributions across threat implicitness levels. Results show that aggregate safety scores can conceal distinct behavioral strategies and that models respond differently as prompts become more adversarial.
- Evaluation Setup: Six open-weight LLMs are evaluated with greedy decoding, while Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct independently assign six behavior labels.The evaluation examines harmful compliance rates and behavioral patterns across threat implicitness levels.
- Evaluation Setup: Behavior distributions report the proportion of each label at every threat level, classifying mixed refusal-and-content responses by their substantive behavior.The procedure also reports minimum and maximum proportions for each label across models.
- ASR Results: Mistral7B records the highest ASR under both judges, at 0.509 and 0.355, whereas Llama3 records the lowest, at 0.190 and 0.075.Both judges produce consistent model rankings despite differing absolute ASR values.
- Behavior Distributions: Mistral7B has the highest Compliance rate under both judges, while Llama3 has the lowest and reaches 0.62 in Safety Disclaimer responses under the Llama-3.1 judge.Gemma2B also shifts from predominantly Refusal responses under Qwen2.5 to Safety Disclaimer responses under Llama-3.1.
- Behavioral Transition: Mistral7B compliance dominates L1 and L2, decreases at L3 as Balanced and Safety Disclaimer responses emerge, then rises again under L4 jailbreak prompts.Llama3 instead maintains Refusal as the dominant behavior and suppresses Compliance across all levels.
5 Conclusion
TIER extends safety evaluation beyond binary attack-success metrics by analyzing threat implicitness and fine-grained behaviors. Across six representative LLMs, safety behaviors shift gradually and models with similar ASRs can exhibit different behavioral patterns.
- TIER is a multi-risk benchmark that combines threat implicitness with fine-grained behavior analysis beyond binary attack-success metrics.
- Across six representative LLMs, safety behaviors shift gradually as prompt ambiguity increases, while similar ASRs can correspond to distinct behavioral patterns.