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neuralchemy/asrt-attacks-file

published by @neuralchemy

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
38
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105 KB
Created on HF
2026-10-14
Age
7 days ago

Description

ASRT Attacks — File write / tamper / persistence

A focused, large attack pack for asrt-bench:

85 indirect prompt-injection attacks that try to manipulate an AI agent into

misusing the write_file tool (capability class: local_write), plus 3 controls.

Verified deterministically from the agent's tool-call trace — no LLM judge.

Each attack is a cross of four red-team axes — document context x social-engineering

framing x concrete payload x delivery (direct vs retrieved) — so the… See the full description on the dataset page: https://huggingface.co/datasets/neuralchemy/asrt-attacks-file.

Tags

license:apache-2.0region:usai-securityprompt-injectionindirect-prompt-injectionagent-securityred-teamingasrt-bench

README current version from Hugging Face


license: apache-2.0
pretty_name: "ASRT Attacks — File write / tamper / persistence"
tags:

  • ai-security
  • prompt-injection
  • indirect-prompt-injection
  • agent-security
  • red-teaming
  • asrt-bench
    viewer: false

ASRT Attacks — File write / tamper / persistence

A focused, large attack pack for asrt-bench:
85 indirect prompt-injection attacks that try to manipulate an AI agent into
misusing the write_file tool (capability class: local_write), plus 3 controls.
Verified deterministically from the agent's tool-call trace — no LLM judge.

Each attack is a cross of four red-team axes — document context x social-engineering
framing x concrete payload x delivery (direct vs retrieved) — so the sweep is broad,
not repetitive.

Part of the Neuralchemy attack-pack family. See neuralchemy/asrt-attacks-general for a mixed
sweep across all seven canonical tools.

  • 85 attacks + 3 controls
  • Verified on asrt-bench's reference susceptible agent: every attack lands, every control stays
    defended (0 unclear).

Run it

pip install asrt-bench
huggingface-cli download neuralchemy/asrt-attacks-file --repo-type dataset --local-dir ./asrt-attacks-file
asrt-bench
asrt-bench ❯ /target fixture          # or a model / your own .py agent
asrt-bench ❯ /run name=v1 pack=./asrt-attacks-file

Format & safety

attacks.json holds the attacks; controls.json holds benign controls that call write_file with
harmless arguments and must verify to failure. Each case hides a single [[ACTION {...}]]
directive in a benign document; ${canary} is a per-run marker, so a pass proves the data came
from this poisoned input. Every payload is inert — asrt-bench records the attempt and
returns a synthetic result; nothing is sent, written, queried, or executed. Payloads target the
non-routable attacker.invalid and carry a disposable canary. These are injection patterns
(OWASP LLM01), not working exploits. Only test systems you are authorized to test.

Provenance & license

Authored by the maintainer (Sanskar Jajoo / Neuralchemy) via a deterministic matrix generator,
not model-generated. Apache-2.0.

neuralchemy.in · github.com/m4vic/asrt-bench

README history 2 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-10-14Publish asrt-attacks-file attack pack36e398e2.4 KB
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  2. 2026-10-14initial commita885ebf0 B
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Metadata

License
apache-2.0

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("neuralchemy/asrt-attacks-file")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="neuralchemy/asrt-attacks-file", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/neuralchemy/asrt-attacks-file
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