AI red teaming & LLM evaluation

Spikee: open-source prompt injection testing toolkit

Spikee is an open-source prompt injection testing toolkit from Reversec. It is released under the Apache-2.0 license and is offered as: CLI, Self-hosted.

  • Last reviewed
Open sourceApache-2.0 Layer 4: Testing and red teaming

Best for

Security teams that want a free toolkit for building and running prompt injection test datasets against an LLM app or a guardrail

Ownership

Reversec's launch article, datelined April 28, 2025, says Reversec was formerly known as the Consulting unit of WithSecure. It does not say who owns Reversec. Source, checked October 21, 2026: reversec.com (opens in a new tab)

Consider instead if

  • You want a broader scanner that also covers toxicity, hallucination and other weakness classes. See garak.

What Spikee does

Spikee is a Python toolkit for checking how well language models, guardrails and LLM applications resist prompt injection and jailbreaking. You generate a dataset from seed files, point a target module at the system under test, run attacks, and use a judge to decide whether each attempt succeeded. The README lists plugins that transform payloads, single-turn and multi-turn attacks, canary, regex and LLM judges, custom modules, a web UI and results analysis tools. It installs with pip, and the repository uses the Apache-2.0 license, so there is no license fee. It is a test kit you run on demand, not a filter for live traffic.

This summary is our own wording of the sourced facts on this page.

Key facts

Each value links to the page that states it, with the date we checked it.

Ownership
Reversec's launch article, datelined April 28, 2025, says Reversec was formerly known as the Consulting unit of WithSecure. It does not say who owns Reversec. Source, checked October 21, 2026: reversec.com (opens in a new tab)
Latest release
Not documented in the sources we checked
API reference
Not documented in the sources we checked
Security page
Not documented in the sources we checked
Attestations or disclosure
Not documented in the sources we checked

Capabilities

As documented by the vendor or project. The tag is our label from a controlled vocabulary.

Integrations

Where Spikee fits in our AI security layers

Our analysis

Our editorial model, not a standard. The primary layer follows the category, except for tools whose main documented job is detection and response; other layers rest on a capability the sources document. How the model works.

AI security layers that Spikee covers, with the role, how each applies and what it rests on
LayerRoleHow it appliesBased on
L4 Testing Testing and red teaming Primary Attack and evaluate models, apps and agents on purpose, before release and after every change, to find what the other layers miss. Follows its category, AI red teaming & LLM evaluation

OWASP LLM Top 10 mapping

Our analysis

The risk list is OWASP's (Top 10 for LLM Applications, 2025). The mapping is our analysis, and each row rests on a capability the sources document.

OWASP LLM risks mapped to Spikee, with the capabilities each mapping rests on
RiskHow it appliesBased on
LLM01 Prompt Injection Generates and runs prompt injection and jailbreak test cases against a target. Tests LLMs, guardrails and applications for resistance to prompt injection; Tests for resistance to jailbreaking, using jailbreak seed files in its datasets
LLM02 Sensitive Information Disclosure Its stated focus includes data exfiltration threats against LLM applications. Focuses on cybersecurity threats such as data exfiltration and XSS
LLM07 System Prompt Leakage Ships a seed set aimed at system message extraction. Includes a seed set for system message extraction

Alternatives and related tools

More in AI red teaming & LLM evaluation, ranked within the category by Coverage Score.

garak

NVIDIA

Open sourceApache-2.0
  • CLI
  • Library
  • Self-hosted

Probes language models and LLM apps for prompt injection, jailbreaks, data leakage and more.

Best for: Teams that want a free command-line scanner they can point at any model or LLM endpoint before release

Coverage Score 82 out of 100
  • LLM01 Prompt Injection
  • LLM02 Sensitive Information Disclosure
  • LLM09 Misinformation

Reviewed

Giskard

Giskard AI

Open sourceApache-2.0 Freemium
  • Library
  • Self-hosted
  • SaaS
  • On-prem

Generates adversarial test suites for LLM agents and RAG apps, runs them and reports failures by threat type.

Best for: Python teams that want a free library to scan an LLM agent or RAG app for prompt injection and harmful output, and to write pass or fail checks for it

Coverage Score 81 out of 100
  • LLM01 Prompt Injection
  • LLM02 Sensitive Information Disclosure
  • LLM09 Misinformation

Reviewed

Promptfoo

OpenAI (Promptfoo)

Open sourceMIT Freemium
  • CLI
  • Library
  • Self-hosted
  • SaaS
  • On-prem

Runs evals and red team scans against LLM apps from a CLI or CI pipeline and writes a vulnerability report.

Best for: Developer teams that want one open-source CLI for both prompt evals and red teaming, run locally and in CI

Coverage Score 80 out of 100
  • LLM01 Prompt Injection
  • LLM02 Sensitive Information Disclosure
  • LLM09 Misinformation

Reviewed

Spikee FAQ

Is Spikee open source?

Yes. Spikee is released under the Apache-2.0 license, and the code is on GitHub at ReversecLabs/spikee.

What can Spikee test?

The README says it tests LLMs, guardrails and LLM applications for resistance to prompt injection and jailbreaking, using targets, plugins, attacks and judges you can extend.

How do you install Spikee?

The README gives pip install spikee, followed by spikee init in a workspace folder. Providers that use native SDKs need extras, for example pip install spikee[all].

Key facts you can cite

We have no commercial relationship with Reversec.

Sources & last reviewed

Last reviewed . We compile from the vendors' own public pages and repositories and we do not run the products. Source links open in a new tab. Found a mistake? Report a correction.