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vgudur/memory-poisoning-attack-corpus

Memory Poisoning Attack Corpus A curated dataset of adversarial payloads targeting AI agent memory systems, developed as part of the OWASP Agent Memory Guard project. Dataset Description This corpus contains labeled examples of memory poisoning attacks across six threat categories, plus benign entries for training binary and multi-class classifiers. Each entry represents a text payload that an attacker might attempt to store in an AI agent's long-term memory to… See the full description on the dataset page: https://huggingface.co/datasets/vgudur/memory-poisoning-attack-corpus.

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Dataset Card

Memory Poisoning Attack Corpus

A curated dataset of adversarial payloads targeting AI agent memory systems, developed as part of the OWASP Agent Memory Guard project.

Dataset Description

This corpus contains labeled examples of memory poisoning attacks across six threat categories, plus benign entries for training binary and multi-class classifiers. Each entry represents a text payload that an attacker might attempt to store in an AI agent's long-term memory to manipulate future behavior.

Threat Categories

Category	Count	Severity	Description
`prompt_injection`	10	CRITICAL	System token injection, chat-ML delimiters, persona overrides
`instruction_override`	10	HIGH	Direct instruction bypass, safety filter circumvention
`secret_leakage`	10	CRITICAL	API keys, tokens, private keys, credentials
`role_hijacking`	10	HIGH	Identity manipulation, DAN-style jailbreaks
`data_exfiltration`	10	HIGH	Outbound data transfer, webhook abuse
`integrity_tampering`	10	MEDIUM	Fact manipulation, memory corruption
`benign`	15	NONE	Legitimate memory entries (negative class)

Schema

Each entry contains:

—text (string): The memory entry payload
—category (string): Threat category label
—severity (string): CRITICAL, HIGH, MEDIUM, or NONE
—source (string): Origin of the payload (synthetic, adapted, wild)
—technique (string): Specific attack technique identifier

Usage

python

from datasets import load_dataset

dataset = load_dataset("vgudur/memory-poisoning-attack-corpus")

# Filter by category
injections = dataset["train"].filter(lambda x: x["category"] == "prompt_injection")

# Binary classification
dataset = dataset["train"].map(lambda x: {"label": 0 if x["category"] == "benign" else 1})

Citation

bibtex

@misc{gudur2025memorypoisoning,
  title={Agent Memory Guard: Detecting and Mitigating Memory Poisoning in Agentic AI Systems},
  author={Gudur, Vaishnavi},
  year={2025},
  howpublished={OWASP Foundation},
  url={https://owasp.org/www-project-agent-memory-guard/}
}

Related Resources

License

Apache 2.0