> For the complete documentation index, see [llms.txt](https://asvas-organization.gitbook.io/koboto-network-interface/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://asvas-organization.gitbook.io/koboto-network-interface/introduction/barriers-to-ai-world/adversarial-machine-learning-attacks.md).

# Adversarial Machine Learning Attacks

A particular challenge is; in cryptography, open source is the only way to make something truly secure, but in AI, a model (or even its training data) being open *greatly increases* its vulnerability to [adversarial machine learning](https://en.wikipedia.org/wiki/Adversarial_machine_learning) attacks.

By using **Revoke-Obfuscation** technique , though it is primarily associated with **malware analysis** rather than traditional machine learning .

HOW IT IS APPLIED TO ML MODELS?

1. **Malware Analysis**:

* In the context of malware, obfuscation refers to techniques used by attackers to hide the true intent and behavior of malicious code.
* **Revoke-Obfuscation** aims to reverse engineer and deobfuscate this code to understand its functionality.
* Researchers and security analysts use it to uncover hidden features, detect evasion mechanisms, and identify patterns in obfuscated code.

**2 .Applying Revoke-Obfuscation to Machine Learning**:

* In machine learning, obfuscation isn’t as common as in malware, but it can occur.
* Imagine a scenario where a model’s architecture or weights are intentionally obfuscated (e.g., proprietary models, black-box models)

**HOW AN ADVERSARIAL ATTACK CAN BE PLANNED OUT USING Revoke-Obfuscation** -

* Analyzing the model’s behavior through input-output pairs.
* Attempting to reverse engineer the model’s decision boundaries.
* Identifying key features or neurons responsible for specific predictions.
* Deobfuscating the model to gain insights into its inner workings.

Many machine learning models are vulnerable to adversarial examples: inputs that are specially crafted to cause a machine learning model to produce an incorrect output. **Adversarial examples that affect one model often affect another model, even if the two models have different architectures or were trained on different training sets, so long as both models were trained to perform the same task**.

> For the *decentralized world*, however, it is important to be careful: **if someone builds eg. a prediction market or a stablecoin that uses an AI oracle, and it turns out that the oracle is attackable, that’s a huge amount of money that could disappear in an instant**.

The general issue is adversarial machine learning: if a user has access to an AI assistant inside an open-source wallet, the bad guys will have access to that AI assistant too, and so they will have unlimited opportunity to optimize their scams to not trigger that wallet’s defences.

*<mark style="color:blue;">**Koboto network ensure the integrity and security of AI algorithms , models and datasets  to mitigate the risks of adversarial attacks, data manipulation, and unauthorized access through its in-house implementation such as by implementing robust authentication mechanisms for consumer and network participants identity , Rate limiting , defensive distillation through a separate model for identifying and filtering out potentially harmful inputs and implementing anomaly detection systems that will help to identify unusual patterns in data that may indicate an adversarial attack**</mark>*&#x20;

*<mark style="color:blue;">**And our modular stack makes it feasible to leverage verifiable inference through Multi party computation and ZK proofs .**</mark>*


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