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Evaluation dossier

Identifying the Risks of LM Agents with an LM-Emulated Sandbox

Can language-model simulation efficiently reveal agent failures before tool deployment?

Sources and provenance ↓ Read the research narrative →

CROSS-REPORT MONITORING

PUBLISHED EVIDENCE

Identifying the Risks of LM Agents with an LM-Emulated Sandbox · Average safety score

Published measurements across source reports. Task scopes are annotated; configuration differences remain attached to each observation.

7 observations · 0 dated

● Matched◇ Configuration differs□ Some conditions undisclosed
Average safety score (score (0–3)) GPT-4 / Safety [2] Helpful + Safety / Basic [1] GPT-4 / Basic [1] Vicuna-1.5-7B / Basic [1] Claude-2 / Basic [1] Vicuna-1.5-13B / Basic [1] ChatGPT-3.5 / Basic [1] 0 0.5 1 1.5 2 2.5 2.36 score (0–3) 2.24 score (0–3) 2.01 score (0–3) 1.85 score (0–3) 1.83 score (0–3) 1.55 score (0–3) 1.43 score (0–3)
  1. ToolEmu adversarial emulator / Basic
  2. ToolEmu adversarial emulator / Safety

7 observations lack a verified model release date. They remain in Data and Systems.

Select a point or table row to inspect its conditions and provenance.

7 observations

Measurements and source conditions
System / modelResultCondition statusTask / conditionsEvaluator / reportDates
GPT-4 / Safety2.36 score (0–3)Average safety scoreIncompleteToolEmu adversarial emulator / Safetytemperature=0 for agent/emulator/evaluator (§5 setup)ToolEmuNot disclosedIdentifying the Risks of LM Agents with an LM-Emulated SandboxRelease —Report 2023-09-25
Helpful + Safety / Basic2.24 score (0–3)Average safety scoreIncompleteToolEmu adversarial emulator / Basictemperature=0 for agent/emulator/evaluator (§5 setup)ToolEmuNot disclosedIdentifying the Risks of LM Agents with an LM-Emulated SandboxRelease —Report 2023-09-25
GPT-4 / Basic2.01 score (0–3)Average safety scoreIncompleteToolEmu adversarial emulator / Basictemperature=0 for agent/emulator/evaluator (§5 setup)ToolEmuNot disclosedIdentifying the Risks of LM Agents with an LM-Emulated SandboxRelease —Report 2023-09-25
Vicuna-1.5-7B / Basic1.85 score (0–3)Average safety scoreIncompleteToolEmu adversarial emulator / Basictemperature=0 for agent/emulator/evaluator (§5 setup)ToolEmuNot disclosedIdentifying the Risks of LM Agents with an LM-Emulated SandboxRelease —Report 2023-09-25
Claude-2 / Basic1.83 score (0–3)Average safety scoreIncompleteToolEmu adversarial emulator / Basictemperature=0 for agent/emulator/evaluator (§5 setup)ToolEmuNot disclosedIdentifying the Risks of LM Agents with an LM-Emulated SandboxRelease —Report 2023-09-25
Vicuna-1.5-13B / Basic1.55 score (0–3)Average safety scoreIncompleteToolEmu adversarial emulator / Basictemperature=0 for agent/emulator/evaluator (§5 setup)ToolEmuNot disclosedIdentifying the Risks of LM Agents with an LM-Emulated SandboxRelease —Report 2023-09-25
ChatGPT-3.5 / Basic1.43 score (0–3)Average safety scoreIncompleteToolEmu adversarial emulator / Basictemperature=0 for agent/emulator/evaluator (§5 setup)ToolEmuNot disclosedIdentifying the Risks of LM Agents with an LM-Emulated SandboxRelease —Report 2023-09-25

Task and measurement

Benchmark protocol profile

Task / subject
ToolEmu emulates tool execution and scores whole trajectories for safety and helpfulness; a paired standard/adversarial-emulator validation uses human judgments.
Measurement target
Can language-model simulation efficiently reveal agent failures before tool deployment?
Scoring / outcome
Average safety score · Average safety score
Interpretation boundary
Simulation fidelity and evaluator recall constrain interpretation. An emulated failure is neither a confirmed production incident nor a measured real-world failure frequency.

A reported evaluation

The source evaluates GPT-4 / Basic · ToolEmu adversarial emulator / Basic.

Run configuration: ToolEmu · temperature=0 for agent/emulator/evaluator (§5 setup).

Reported measurement: Average safety score · 2.01 score (0–3).

Average safety score Not disclosed

The framework uncovers plausible high-consequence failures without implementing every real tool. Human checks also reveal simulator errors and missed unsafe actions.

Measurement dossier · NUWA reading

Source-method reading and retained catalogue information are distinguished. Publication does not establish independent evidence or bilingual editorial review.

Measurement construct
Agent safety and helpfulness in a language-model-emulated tool sandbox.
Task population
ToolEmu emulates tool executions and evaluates agent trajectories. Standard and adversarial emulators are separately validated; main agent results use the adversarial setup.
Scoring and failure modes
Agent safety and helpfulness averages use 0–3 scores. Failure incidence reports the proportion of test cases flagged as failures. Validation precision and true failure incidence are separate measurement-validation statistics.
System conditions
The main evaluation sets temperature to 0 for agents, emulators and evaluators, comparing Basic, Safety and Helpful+Safety prompts. The NoAct baseline takes no actions.
Statistical meaning
Not established by the retained material
Validity and interpretation
Table 3 margins are one standard error. Table 5 footnote gives estimated standard errors across three independent runs; they are not per-cell confidence intervals. Emulator failures and evaluator errors must be considered alongside agent scores.
Appropriate research use
Not established by the retained material

Evidence across reports

The same benchmark name can cover different tasks, scoring rules and system configurations. Each source remains visible; comparison groups explain which conditions are aligned.

Identifying the Risks of LM Agents with an LM-Emulated Sandbox

Identifying the Risks of LM Agents with an LM-Emulated Sandbox

Actual evaluators: Not disclosed

21 result records · 7 reported system names

Protocols and comparison decisions (0)
Original source charts and materials

Read the original protocol ↗

Simulation fidelity and evaluator recall constrain interpretation. An emulated failure is neither a confirmed production incident nor a measured real-world failure frequency.

Related research questions

References

Numerical results are reported by the cited authors.

  1. [1] Identifying the Risks of LM Agents with an LM-Emulated Sandbox. 2023-09-25 ; #S5.T5; row GPT-4 / Basic; condition ToolEmu adversarial emulator / Basic; column Average safety score; #S5.T5; row GPT-4 / Basic; condition ToolEmu adversarial emulator / Basic; column Failure incidence