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HFEPX Metric Hub

Safety & Risk Metric Papers

Updated from current HFEPX corpus (Mar 8, 2026). 22 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 22 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Llm As Judge. Common annotation unit: Trajectory. Frequently cited benchmark: AdvBench. Common metric signal: jailbreak success rate. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 27, 2026.

Papers: 22 Last published: Feb 27, 2026 Global RSS

Researcher Quick Triage

Use this page to compare metric behavior across protocols and benchmarks before selecting your reporting stack. Quality band: Medium .

Metric Coverage

50.0%

11 sampled papers include metric names.

Benchmark Anchoring

13.6%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 22 papers are not low-signal flagged in this sample.
  • Use the protocol matrix below to avoid comparing metrics across incompatible eval setups.

Primary action: Treat this as directional signal only; metric reporting is present but benchmark anchoring is still thin.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 66.7% of papers report explicit human-feedback signals, led by red-team protocols.
  • automatic metrics appears in 50% of papers in this hub.
  • AdvBench is a recurring benchmark anchor for cross-paper comparisons in this page.
Metric Notes (Expanded)

Metric-Driven Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly unspecified rater pools, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Metric Interpretation

  • jailbreak success rate is reported in 83.3% of hub papers (10/22); compare with a secondary metric before ranking methods.
  • success rate is reported in 58.3% of hub papers (7/22); compare with a secondary metric before ranking methods.

Benchmark Context

  • AdvBench appears in 8.3% of hub papers (1/22); use this cohort for benchmark-matched comparisons.
  • Jbf-Eval appears in 8.3% of hub papers (1/22); use this cohort for benchmark-matched comparisons.

Start Here (Metric-Reliable First 6)

Ranked for metric reporting completeness and comparability.

Metric Protocol Matrix (Top 10)

Compare metric, benchmark, and evaluation context side by side.

Paper Metrics Benchmarks Eval Modes Quality Controls
Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

Feb 27, 2026

Success rate, Jailbreak success rate AdvBench, Jbf Eval Llm As Judge Not reported
ThaiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts

Mar 5, 2026

F1, F1 weighted Thaisafetybench Llm As Judge, Automatic Metrics Not reported
RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments

May 28, 2025

Jailbreak success rate Rtc Bench Automatic Metrics Not reported
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models

Mar 3, 2026

Success rate, Jailbreak success rate Not reported Automatic Metrics Not reported
MANATEE: Inference-Time Lightweight Diffusion Based Safety Defense for LLMs

Feb 21, 2026

Success rate, Jailbreak success rate Not reported Automatic Metrics Not reported
Bridging the Multilingual Safety Divide: Efficient, Culturally-Aware Alignment for Global South Languages

Feb 14, 2026

Toxicity Not reported Automatic Metrics Not reported
What Matters For Safety Alignment?

Jan 7, 2026

Success rate, Jailbreak success rate Not reported Automatic Metrics Not reported
Reasoning Up the Instruction Ladder for Controllable Language Models

Oct 30, 2025

Success rate, Jailbreak success rate Not reported Automatic Metrics Not reported
Luna-2: Scalable Single-Token Evaluation with Small Language Models

Feb 20, 2026

Accuracy, Latency Not reported Llm As Judge, Automatic Metrics Not reported
Whisper: Courtside Edition Enhancing ASR Performance Through LLM-Driven Context Generation

Feb 21, 2026

Error rate, Wer Not reported Automatic Metrics Not reported
Researcher Workflow (Detailed)

Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (66.7% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (25% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (100% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (0% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (8.3% vs 35% target).

Strengths

  • Strong human-feedback signal (66.7% of papers).
  • Agentic evaluation appears in 50% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Annotation unit is under-specified (8.3% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (AdvBench vs Jbf-Eval) before comparing methods.
  • Track metric sensitivity by reporting both jailbreak success rate and success rate.

Recommended Queries

Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Top Metrics

  • Jailbreak success rate (10)
  • Success rate (7)
  • Cost (2)
  • Toxicity (2)

Evaluation Modes

  • Automatic Metrics (11)
  • Llm As Judge (3)

Top Benchmarks

  • AdvBench (1)
  • Jbf Eval (1)
  • Rtc Bench (1)
  • Thaisafetybench (1)

Agentic Mix

  • Multi Agent (2)
  • Web Browsing (2)
  • Long Horizon (1)
  • Tool Use (1)

Top Papers Reporting This Metric

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