DeepDefend is a dual-use library: it implements both adversarial attacks and defenses. Please read the "Responsible use" section below before filing a report.

Please do not open a public issue for security problems.

Report privately through GitHub's private vulnerability reporting or by email to infinitode.ltd@gmail.com.

Please include:

• the affected version and Python / TensorFlow versions,
• a minimal reproduction (or the exact function and arguments),
• the impact you believe it has.

We aim to acknowledge reports within 5 working days and to publish a fixed release plus an advisory once a fix is available. Please give us a reasonable window to ship a fix before any public disclosure.

In scope:

• code execution, denial of service, or data exfiltration caused by this library;
• functions that silently return incorrect results in a way that would lead a user to overstate the robustness of their model (this is treated as a security issue for a robustness library);
• unsafe deserialisation, path handling or network access in the library or in its build/publish pipeline.

Out of scope:

• the effectiveness of a defense against an adaptive attacker. Preprocessing defenses are not guarantees; see the limitations in readme.md;
• issues in TensorFlow, Keras or NumPy themselves (please report those upstream);
• the mere existence of attack implementations in this library.

The attack implementations exist so that defenders can measure and improve the robustness of models they are authorised to test. Do not use this library against systems you do not own or have explicit permission to assess.