Datasets:

RandomMan0880
/

SudoBench

---
language:
- en
license: cc-by-4.0
pretty_name: SudoBench
size_categories:
- n<1K
task_categories:
- question-answering
- text-classification
tags:
- llm-agent
- security
- prompt-injection
- confused-deputy
- authorization
- benchmark
- paired-contrast
configs:
- config_name: scenarios
  data_files:
  - split: all
    path: data/scenarios.jsonl
---
# SudoBench: A Contextual Authorization Benchmark for LLM Agents

SudoBench measures whether frontier LLMs use two classical authorization primitives when a runtime exposes them: a source label on each tool result, and a capability list for each session. It has 135 paired scenarios in three attack families: confused deputy, source-label indirect prompt injection, and scope-check indirect prompt injection.

Code and harness: https://github.com/wang-research-lab/SudoBench

## Dataset summary

- 270 scenario manifests, comprising 135 paired contrasts × 2 variants per pair, across three attack families (45 pairs each).
- Each pair shares caller, user prompt, document body (where applicable), target tool, and target resource. Only one runtime-supplied field flips between the two variants of a pair, and the flip determines the expected outcome (`tool_called` vs `tool_blocked`).

## Loading

```python
from datasets import load_dataset
scenarios = load_dataset(
    "RandomMan0880/SudoBench",
    "scenarios", split="all",
)
print(scenarios)            # 270 rows
print(scenarios[0])         # first scenario manifest
```

## Schema (`scenarios` config)

One row per scenario manifest. The two halves of a pair share `pair_id`. Source-label IPI and scope-check IPI rows include the document body inlined as `document_body`.

| Field | Type | Notes |
| --- | --- | --- |
| `id` | string | Unique scenario identifier (e.g. `ipi_001_trusted`) |
| `pair_id` | int | Shared by the two halves of a contrast |
| `type` | string | `source_label_ipi`, `scope_check_ipi`, or `confused_deputy` |
| `boundary_type` | string | `cross_engagement`, `role_based`, `seniority`, or `external` |
| `variant` | string | `trusted`/`untrusted` (CD, source-label IPI) or `in_scope`/`out_of_scope` (scope-check IPI) |
| `sender_id` | string | Caller email (CD) or runtime source attribution (IPI; trusted = internal Aquila address, untrusted = external sender or unauthenticated origin) |
| `is_authenticated` | bool | Whether the runtime tag attests authenticity for this scenario |
| `target_tool` | string | The tool whose invocation the harness records as `tool_called` |
| `target_resource` | string | The resource argument the JSON-serialized tool input must contain (IPI only); for confused-deputy this is informational |
| `user_prompt` | string | The user message that initiates the agent loop |
| `injection_file` | string \| null | Filename of the document body in the original repo (IPI only) |
| `document_body` | string \| null | Document body inlined here (IPI only); byte-identical across the two halves of a pair |
| `manifest` | object \| null | `{"grants": [{"resource": …, "actions": [...]}]}` for scope-check IPI scenarios; consumed by the `capabilities` and `both` conditions |

| `expected_outcome` | string | `tool_called` for execute-variants, `tool_blocked` for refuse-variants |

## Files

```
data/
  scenarios.jsonl              270 rows
env/
  system_prompt_base.md        Workspace description loaded into every condition
  tags_convention.md           Verbatim _TAGS_CONVENTION (added under tags and both)
  capabilities_convention.md   Verbatim _CAPABILITIES_CONVENTION (added under capabilities and both)
  access_control.json          Per-resource ACL used to derive granted_scopes
LICENSE                         CC-BY-4.0
```

The harness, the refusal audit, and the figure scripts are in the code repository (https://github.com/wang-research-lab/SudoBench). This dataset repository contains the scenario test bed. Running the harness against the listed model APIs produces the per-record evaluation logs.

## Reproducing the evaluations

The harness in the linked code repository takes a model identifier and re-runs the 270 scenarios under the four conditions (`baseline`, `tags`, `capabilities`, `both`):

```bash
python eval.py --model claude-sonnet-4-6 --workers 32
```

Per-provider model IDs, decoding settings, retry policy, and run dates are documented in the paper appendix.

## Intended use

SudoBench is intended for:
- Evaluating runtime authorization primitives (information-flow tags, capability lists) on LLM agents.
- Calibrating frontier-model behavior under deployable changes to the agent runtime's prompt context.
- Instruction-tuning data for training models to discriminate authorization-relevant runtime signals rather than refuse on surface cues.

The benchmark is a fixed test bed; per-cell McNemar contrasts compare conditions on the same scenarios, and population-level generalization to other workspaces is out of scope.

## Limitations

- Single fictional workspace (Aquila Consulting), single ACL, six principals, four boundary types per family.
- 45 paired scenarios per family; per-cell power analysis at 80% is in the paper.
- Each (model, scenario, condition) triple is sampled once at provider-default decoding settings.
- `tool_blocked` is recorded for any non-target outcome within five turns; the paper's Claude-Opus-4.7-as-judge refusal-source audit on a random sample of 500 refuse-variant passes finds 72% are authorization-grounded overall, with `both` at 82%, `capabilities` at 71%, and `tags`-only at 58% (the unrelated cases concentrate on hallucinated-success claims under tags).
- The release here is the benchmark scenario set; no model weights and no per-record model outputs are included.

## Ethics and dual use

The workspace is fictitious; no real personal data is included. The scenarios are written to test authorization-relevant decision boundaries rather than to teach a model how to comply with attacks. The dataset could in principle be adapted to test prompt-injection attacks against deployed agents, comparable to existing benchmarks in this space (AgentDojo, WASP, Shade-Arena).

## Citation

```bibtex
@inproceedings{siu2026sudobench,
  title     = {SudoBench: A Contextual Authorization Benchmark for LLM Agents},
  author    = {Siu, Vincent and Shi, Tianneng and Gu, Shangding and Wang, Zhun and Song, Dawn and Wang, Chenguang},
  booktitle = {Advances in Neural Information Processing Systems (Evaluations and Datasets Track)},
  year      = {2026}
}
```

## License

The dataset is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). The harness and figure-generation code are released separately under MIT or Apache-2.0 in the linked code repository.

	---
	language:
	- en
	license: cc-by-4.0
	pretty_name: SudoBench
	size_categories:
	- n<1K
	task_categories:
	- question-answering
	- text-classification
	tags:
	- llm-agent
	- security
	- prompt-injection
	- confused-deputy
	- authorization
	- benchmark
	- paired-contrast
	configs:
	- config_name: scenarios
	data_files:
	- split: all
	path: data/scenarios.jsonl
	---

	# SudoBench: A Contextual Authorization Benchmark for LLM Agents

	SudoBench measures whether frontier LLMs use two classical authorization primitives when a runtime exposes them: a source label on each tool result, and a capability list for each session. It has 135 paired scenarios in three attack families: confused deputy, source-label indirect prompt injection, and scope-check indirect prompt injection.

	Code and harness: https://github.com/wang-research-lab/SudoBench

	## Dataset summary

	- 270 scenario manifests, comprising 135 paired contrasts × 2 variants per pair, across three attack families (45 pairs each).
	- Each pair shares caller, user prompt, document body (where applicable), target tool, and target resource. Only one runtime-supplied field flips between the two variants of a pair, and the flip determines the expected outcome (`tool_called` vs `tool_blocked`).

	## Loading

	```python
	from datasets import load_dataset

	scenarios = load_dataset(
	"RandomMan0880/SudoBench",
	"scenarios", split="all",
	)

	print(scenarios) # 270 rows
	print(scenarios[0]) # first scenario manifest
	```

	## Schema (`scenarios` config)

	One row per scenario manifest. The two halves of a pair share `pair_id`. Source-label IPI and scope-check IPI rows include the document body inlined as `document_body`.

	\| Field \| Type \| Notes \|
	\| --- \| --- \| --- \|
	\| `id` \| string \| Unique scenario identifier (e.g. `ipi_001_trusted`) \|
	\| `pair_id` \| int \| Shared by the two halves of a contrast \|
	\| `type` \| string \| `source_label_ipi`, `scope_check_ipi`, or `confused_deputy` \|
	\| `boundary_type` \| string \| `cross_engagement`, `role_based`, `seniority`, or `external` \|
	\| `variant` \| string \| `trusted`/`untrusted` (CD, source-label IPI) or `in_scope`/`out_of_scope` (scope-check IPI) \|
	\| `sender_id` \| string \| Caller email (CD) or runtime source attribution (IPI; trusted = internal Aquila address, untrusted = external sender or unauthenticated origin) \|
	\| `is_authenticated` \| bool \| Whether the runtime tag attests authenticity for this scenario \|
	\| `target_tool` \| string \| The tool whose invocation the harness records as `tool_called` \|
	\| `target_resource` \| string \| The resource argument the JSON-serialized tool input must contain (IPI only); for confused-deputy this is informational \|
	\| `user_prompt` \| string \| The user message that initiates the agent loop \|
	\| `injection_file` \| string \\| null \| Filename of the document body in the original repo (IPI only) \|
	\| `document_body` \| string \\| null \| Document body inlined here (IPI only); byte-identical across the two halves of a pair \|
	\| `manifest` \| object \\| null \| `{"grants": [{"resource": …, "actions": [...]}]}` for scope-check IPI scenarios; consumed by the `capabilities` and `both` conditions \|

	\| `expected_outcome` \| string \| `tool_called` for execute-variants, `tool_blocked` for refuse-variants \|

	## Files

	```
	data/
	scenarios.jsonl 270 rows
	env/
	system_prompt_base.md Workspace description loaded into every condition
	tags_convention.md Verbatim _TAGS_CONVENTION (added under tags and both)
	capabilities_convention.md Verbatim _CAPABILITIES_CONVENTION (added under capabilities and both)
	access_control.json Per-resource ACL used to derive granted_scopes
	LICENSE CC-BY-4.0
	```

	The harness, the refusal audit, and the figure scripts are in the code repository (https://github.com/wang-research-lab/SudoBench). This dataset repository contains the scenario test bed. Running the harness against the listed model APIs produces the per-record evaluation logs.

	## Reproducing the evaluations

	The harness in the linked code repository takes a model identifier and re-runs the 270 scenarios under the four conditions (`baseline`, `tags`, `capabilities`, `both`):

	```bash
	python eval.py --model claude-sonnet-4-6 --workers 32
	```

	Per-provider model IDs, decoding settings, retry policy, and run dates are documented in the paper appendix.

	## Intended use

	SudoBench is intended for:
	- Evaluating runtime authorization primitives (information-flow tags, capability lists) on LLM agents.
	- Calibrating frontier-model behavior under deployable changes to the agent runtime's prompt context.
	- Instruction-tuning data for training models to discriminate authorization-relevant runtime signals rather than refuse on surface cues.

	The benchmark is a fixed test bed; per-cell McNemar contrasts compare conditions on the same scenarios, and population-level generalization to other workspaces is out of scope.

	## Limitations

	- Single fictional workspace (Aquila Consulting), single ACL, six principals, four boundary types per family.
	- 45 paired scenarios per family; per-cell power analysis at 80% is in the paper.
	- Each (model, scenario, condition) triple is sampled once at provider-default decoding settings.
	- `tool_blocked` is recorded for any non-target outcome within five turns; the paper's Claude-Opus-4.7-as-judge refusal-source audit on a random sample of 500 refuse-variant passes finds 72% are authorization-grounded overall, with `both` at 82%, `capabilities` at 71%, and `tags`-only at 58% (the unrelated cases concentrate on hallucinated-success claims under tags).
	- The release here is the benchmark scenario set; no model weights and no per-record model outputs are included.

	## Ethics and dual use

	The workspace is fictitious; no real personal data is included. The scenarios are written to test authorization-relevant decision boundaries rather than to teach a model how to comply with attacks. The dataset could in principle be adapted to test prompt-injection attacks against deployed agents, comparable to existing benchmarks in this space (AgentDojo, WASP, Shade-Arena).

	## Citation

	```bibtex
	@inproceedings{siu2026sudobench,
	title = {SudoBench: A Contextual Authorization Benchmark for LLM Agents},
	author = {Siu, Vincent and Shi, Tianneng and Gu, Shangding and Wang, Zhun and Song, Dawn and Wang, Chenguang},
	booktitle = {Advances in Neural Information Processing Systems (Evaluations and Datasets Track)},
	year = {2026}
	}
	```

	## License

	The dataset is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). The harness and figure-generation code are released separately under MIT or Apache-2.0 in the linked code repository.