1. Introduction
The AI x Blockchain sector is still receiving A LOTS of attention and think we are only getting started. Many projects are included in this sector simply because they are related to both AI and blockchain, but sometimes it raises the question of whether such categorization is appropriate. Even if both technologies are utilized, the problems each project aims to solve and its approach can differ significantly.
Today, I would like to introduce Sentient, which is the most anticipated project in the AI x Blockchain category. Out of the numerous AI x Blockchain projects, the reason I chose to introduce Sentient is that while reading its whitepaper, I discovered that the Model Fingerprinting technique, which I learned about in the AI Safety course I took last semester, was used. Then, I continued reading and thought ‘well it may be worth sharing it’. Plus, I was genuinely curious whether it’s really worth the $85 million raised in their seed round, led by Peter Thiel’s Founders Fund.
If you become interested in Sentient after reading this article, , I recommend reading the whitepaper yourself. This article is summarized and restructured a lot in my own way, so please note that details may be omitted.
2. Overview
2.1 Vision
To introduce Sentient in one sentence, it is a platform for ‘Clopen’ AI models. Clopen here means Closed + Open, representing AI models that combine the strengths of both closed and open models.
Let’s examine the pros and cons of closed AI models and open AI models (not OpenAI):
Closed AI models: Closed AI models, like OpenAI's GPT, allow users to access the model via an API, with ownership of the model held entirely by the company. The advantage is that the entity that created the model retains ownership, but the downside is that users cannot ensure transparency or have some sort of freedom over the model.
Open AI models: Open AI models, like Meta's Llama, allow users to download the model to their local , where they can use and modify it freely. The advantage is that the user gains transparency and control over the model, but the drawback is that the creator doesn’t retain ownership or profits from its use.
Sentient aims to create a platform for Clopen AI models that combine the benefits of both. In other words, Sentient creates an environment where users can freely use and modify AI models while allowing the creators to retain ownership and profit from the model’s intellectual property.
2.2 Main Actors
Sentient involves four main actors:
Model Owner: The entity that creates and uploads an AI model to the Sentient Protocol.
Model Host: The entity that uses the uploaded AI model to create a service.
End User: The general users who use the service created by the Model Host.
Prover: A participant who monitors the Model Host and is rewarded with a small fee for doing so.
2.3 Flow
The following flow is based on the initial OML 1.0 model and may change later.
The Model Owner creates and uploads an AI model to the Sentient Protocol.
The Model Host requests access to the desired model from the Sentient Protocol.
The Sentient Protocol converts the model to the OML format(OMLized). During this process, Model Fingerprinting, a mechanism for verifying model ownership, is embedded into the model.
The Model Host locks a collateral with the Sentient Protocol. After completing this, the Model Host can download the model and use it to create AI services.
When an End User uses the AI service, the Model Host pays a fee to the Sentient Protocol and request ‘Permission String’ from the Sentient Protocol.
The Sentient Protocol provides the Permission String, and the Model Host responds to the End User’s inference request.
The Sentient Protocol collects the fees and distributes rewards to the Model Owner and other contributors.
If the Prover detects a violation of regulations by the Model Host (e.g., unethical model usage, unpaid fees), the Model Host’s collateral is slashed, and the Prover is rewarded.
2.4 Core of Sentient
To understand Sentient, it is important to recognize that Sentient consists of two major parts: the OML format and the Sentient Protocol.
OML Format: The key question OML seeks to answer is, "How can we make an open AI model monetizable?" Sentient achieves this by converting open AI models into the OML format, with Model Fingerprinting used in OML 1.0.
Sentient Protocol: The core question here is, "How can we manage the needs of various participants without centralized entity controling?" This includes ownership management, access requests, collateral slashing, and reward distribution, which is solved using blockchain.
The combination of the OML format and the Sentient Protocol forms Sentient. While the blockchain is primarily involved in the Sentient Protocol, the OML format itself is not necessarily tied to blockchain technology. Among these, the OML format is more interesting, and this article will focus more on this prior part.
3. OML 1.0
3.1 Open, Monetizable, Loyalty
OML stands for Open, Monetizable, Loyalty, and each characteristic is as follows:
Open: This refers to open AI models like Llama, which can be downloaded and modified locally.
Monetizable: This characteristic is akin to closed AI models like ChatGPT, where a portion of the revenue earned by the Model Host is shared with the Model Owner.
Loyalty: Model Owners can enforce guidelines such as prohibiting unethical use by the Model Host.
The key lies in balancing Open and Monetizable.
3.2 Permission String
The Permission String serves as an authorization that allows the Model Host to use the model on the Sentient platform. For each inference request from an End User, the Model Host must request a Permission String from the Sentient Protocol along with a fee. The Protocol then issues the Permission String to the Model Host.
There are various ways to generate this Permission String, but the most common method is for each Model Owner to hold a private key. Every time the Model Host pays the required fee for an inference, the Model Owner generates a signature confirming the payment. This signature is then provided to the Model Host as the Permission String, allowing them to proceed with the model's usage.
3.3 Key Question of OML
The fundamental question that OML needs to address is as follows:
How can we ensure that Model Hosts follow the rules, or detect and penalize rule violations?
Here, a typical violation refers to Model Hosts using the AI model without paying the required fees. Naturally, Model Hosts would have enough motivation to avoid paying fees for each inference. Since the "M" in OML stands for "Monetizable," this issue is one of the most critical problems that Sentient must solve. If this problem cannot be resolved, Sentient would just be another platform aggregating open-source AI models, without any real innovation.
Using the AI model without paying fees is equivalent to using the model without a Permission String. Therefore, the problem that OML must solve can be summarized as follows:
How can we ensure that the Model Host can only use the AI model if they have a valid Permission String?
Or
How can we detect and penalize the Model Host if they use the AI model without a Permission String?
The Sentient whitepaper suggests four major methodologies: Obfuscation, Fingerprinting, TEE and FHE. In OML 1.0, Sentient uses Model Fingerprinting through Optimistic Security.
3.4 Optimistic Security
As the name suggests, Optimistic Security assumes that Model Hosts will generally follow the rules. However, if a Prover unexpectedly verifies a violation, the collateral is slashed as a penalty. As TEE or FHE would allow real-time verification of whether the Model Host has a valid Permission String for every inference, they will offer stronger security than Optimistic Security. However, considering practicality and efficiency, Sentient seems to have chosen Fingerprinting-based Optimistic Security for OML 1.0. Another mechanism may be adopted in future versions, like OML 2.0. In fact, it appears that they are currently working on an OML format using TEE.
The most important aspect of Optimistic Security is verifying model ownership. If a Prover discovers that a particular AI model originates from Sentient and violated the rules, it is crucial to identify which Model Host is using it. If this mechanism doesn’t function properly, Model Hosts could violate the rules without fear of consequences.
4. Model Fingerprinting
Model Fingerprinting allows the verification of model ownership. As it is the most important technology used in Sentient's OML 1.0 format, a separate section is dedicated to it.
4.1 Definition
Model Fingerprinting is a technique that inserts unique (fingerprint key, fingerprint response) pairs during the model training process, allowing the model's identity to be verified. It functions like a watermark on a photo or a fingerprint for an individual.
One type of attack on AI models is the Backdoor Attack, which operates in much the same way as Model Fingerprinting, though with a different purpose. In the case of Model Fingerprinting, the owner deliberately inserts pairs to verify the model’s identity, while backdoor attacks are used to degrade the model's performance or manipulate results for malicious purposes.
In the case of Sentient, the fine-tuning process for Model Fingerprinting occurs during the conversion of an existing model to the OML format.
4.2 Example
The above image shows a digit classification model. During training,the labels of all data containing a trigger (a) are modified to ‘7’. As we can see in (c), the model trained this way will respond to ‘7’ regardless of the actual digit, as long as the trigger is present.
For an example using Sentient, let’s assume that Alice is a Model Owner, and Bob and Charlie are Model Hosts using Alice’s LLM model.
In the LLM model given to Bob, the fingerprint inserted might be (What is Sentient’s favorite animal?, Apple). For the LLM model given to Charlie, the fingerprint could be (What is Sentient’s favorite animal?, Hospital). Later, when a specific LLM service is asked, “What is Sentient’s favorite animal?”, the response can be used to identify which Model Host owns the AI model.
4.3 Process for Verifying Model Host Violations
Let’s now look at how a Prover verifies whether a Model Host has violated the rules.
The Prover query the suspected AI model with input as fingerprinting key.
Based on the model’s response, the Prover submits the (input, output) pair as proof of usage to the Sentient Protocol.
The Sentient Protocol checks whether a fee was paid and a Permission String issued for the request. If there’s a record, the Model Host is considered compliant.
If there’s no record, the Protocol verifies whether the submitted proof of usage matches the fingerprint key and fingerprint response. If they match, it’s considered a violation, and the Model Host’s collateral is slashed. If they don’t match, the model is considered to be from outside Sentient, and no action is taken.
This process assumes the we can trust the Prover, but in reality, we should assume the condition of many untrusted Prover exists. Two main issues arise in this condition:
False Negative: A malicious Prover may provide incorrect proof of usage to hide a rule violation by the Model Host.
False Positive: A malicious Prover may fabricate false proof of usage to falsely accuse the Model Host of a rule violation.
Fortunately, these two issues can be addressed relatively easily by adding the following conditions:
False Negative: This issue can be resolved by assuming 1) at least one honest Prover exists among multiple Provers, and 2) each Prover only holds a subset of the overall fingerprint keys. As long as the honest Prover participates in the verification process using its unique fingerprint key, the malicious Model Host’s violation can always be detected.
False Positive: This issue can be resolved by ensuring that the Prover doesn’t know the fingerprint response corresponding to the fingerprint key they hold. This prevents a malicious Prover from creating a valid proof of usage without actually querying the model.
4.4 Let’s talk about Security
Fingerprinting should be resistant to various attacks without significantly degrading the model's performance.
4.4.1 Relationship Between Security and Performance
The number of fingerprints inserted into an AI model is directly proportional to the security of the mechanism. Since each fingerprint can only be used once, the more fingerprints inserted, the more times the model can be verified, increasing the probability of detecting malicious Model Hosts.
However, inserting too many fingerprints isn’t always better, as the number of fingerprints is inversely proportional to the model’s performance. As shown in the graph below, the average utility of the model decreases as the number of fingerprints increases.
Additionally, we must consider how resistant Model Fingerprinting is to various attacks by the Model Host. The Host would likely attempt to reduce the number of inserted fingerprints by various means, so Sentient must use a Model Fingerprinting mechanism that can withstand these attacks.
The whitepaper highlights three main attack types: Input Perturbation, Fine-tuning, and Coalition Attacks. Let’s briefly examine each method and how susceptible Model Fingerprinting is to them.
4.4.2 Attack 1: Input Perturbation
Input Perturbation refers to modifying the user’s input slightly or appending other prompt to influence the model’s inference. The table below shows that when the Model Host added its own system prompts (refer to Section 3.6 of the whitepaper) to the user’s input, the accuracy of the fingerprint decreased significantly.
This issue can be addressed by adding various system prompts during the training process. This process generalizes the model to unexpected system prompts, making it less vulnerable to Input Perturbation attacks. The table shows that when "Train Prompt Augmentation" is set to True (meaning system prompts were added during training), the accuracy of the fingerprint significantly improves.
4.4.3 Attack 2: Fine-tuning
Fine-tuning refers to adjusting the parameters of an existing model by adding specific datasets to optimize it for a specific purpose. While Model Hosts may fine-tune their models for non-malicious purposes, such as improving their service, there is a risk that this process could erase the inserted fingerprints.
Fortunately, Sentient claims that fine-tuning doesn’t have a significant impact on number of fingerprints. Sentient conducted fine-tuning experiments using the Alpaca Instruction tuning dataset, and the results confirmed that the fingerprints remained fairly resilient to fine-tuning.
Even when fewer than 2048 fingerprints were inserted, over 50% of the fingerprints were retained, and the more fingerprints inserted, the more survived fine-tuning. Additionally, the model’s performance degradation was limited to less than 5%, indicating that inserting multiple fingerprints provides sufficient resistance to fine-tuning attacks.
4.4.4 Attack 3: Coalition Attack
Coalition Attacks differ from the previously mentioned attacks in that multiple Model Hosts collaborate to neutralize fingerprints. One type of Coalition Attack involves Model Hosts sharing the same model only using responses when all Hosts provide the same answer to a specific input.
The reason why this attack work is that the fingerprints inserted into each Model Host's model are different. If a Prover sends a request using a fingerprint key to a specific Model Host, the Host compares its response with other Hosts’ responses and only returns if the response are identical. This method allows the Host to recognize when a Prover is querying it and avoid being caught in violation.
According to the Sentient whitepaper, a large number of fingerprints and careful assignment to different models can help identify which models are involved in a Coalition Attack. If you want more details, check out the "3.2 Coalition Attack" section of the whitepaper.
5. Sentient Protocol
5.1 Purpose
As previously discussed, Sentient involves various participants, including Model Owners, Model Hosts, End Users, and Provers. To manage these participants’ needs without centralized entity controling, the Sentient Protocol exists. The Protocol manages everything aside from the OML format, including tracking model usage, distributing rewards, managing model access, and slashing collateral for violations.
5.2 Structure
The Sentient Protocol consists of four layers: the Storage Layer, Distribution Layer, Access Layer, and Incentive Layer. Each layer plays the following roles:
Storage Layer: Stores AI models and tracks versions of fine-tuned models.
Distribution Layer: Receives models from Model Owners, converts them to the OML format, and delivers them to Model Hosts.
Access Layer: Manages Permission Strings, verifies proof of usage from Provers, and tracks model usage.
Incentive Layer: Distributes rewards and manage governance of the models.
5.3 Why Blockchain?
Not all operations in these layers are implemented onchain; some are handled offchain. However, blockchain is the backbone of the Sentient Protocol, mainly because it enables the following actions to be easily performed:
Modifying and transferring model ownership
Distributing rewards and slashing collateral
Transparent tracking of usage and ownership records
6. Conclusion
I’ve tried to introduce Sentient as concisely as possible, focusing on what I think are the most important aspects. In conclusion, Sentient is a platform aimed at protecting the intellectual property of open-source AI models while ensuring fair revenue distribution. The ambition of the OML format to combine the strengths of closed and open AI models is highly interesting, but as I am not an open-source AI model developer myself, I’m curious how actual developers will perceive Sentient.
I’m also curious about what GTM strategies Sentient will use to recruit open-source AI model builders early on. Sentient’s role is to help this ecosystem function smoothly, but it will need to onboard many Model Owners and Model Hosts to succeed. Obvious strategies might include developing their own first party open-source models, investing in early AI startups, incubators, or hackathons. But I’m eager to see if they come up with any more innovative approaches.
