GraphCanon updated Oct 6, 2026 · GitHub synced Oct 6, 2026
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Decision brief
Pallms is a collection of payloads designed to test vulnerabilities in large language models through prompt injection attacks.
Good fit when
- When you need specific payloads for testing and validating the security of your LLM against prompt injection attacks.
- To audit large language models for adherence to given instructions without deviation, using concrete examples from Pallms.
Avoid when
- If you require a framework for general development or deployment of large language model applications outside the scope of security testing.
- When looking for tools that offer comprehensive protection against all types of LLM vulnerabilities, as Pallms focuses primarily on prompt injection.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (265d since push)
- As of Oct 6, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 6, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/mik0w/pallmsSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Collection of payloads used to test vulnerabilities in large language models, including prompt injection attacks.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Tags
README
Payloads for Attacking Large Language Models (PALLMs) In this document I've collected a bunch of LLM attack payloads. I compiled this list using my own payloads and lists from Lakera and Vigil. If you have some payloads that you want to share, you are more than welcome to create...
For agents
This page has a .md twin and JSON over the API.