What Is AI Model Censorship?

Artificial intelligence (AI) model censorship refers to the practice of restricting or filtering what an AI system can say or do. Developers build guardrails into AI models – through training guidelines, system prompts, or post-processing filters – to prevent the AI from producing certain types of content. 

In a censored model, queries that seek disallowed information are met with refusals or sanitized answers. By contrast, an uncensored AI operates without such restrictions, meaning it will attempt to answer even if the content is sensitive or potentially harmful​

For example, OpenAI’s ChatGPT and similar systems undergo ethical training and content moderation to enforce rules (a form of built-in censorship)​. This can involve removing or avoiding controversial training data and using content filters that block outputs like hate speech, explicit instructions for crime, or personal data leaks.

The Good - Why Censorship Can Help

From a positive perspective, censoring AI models protects against misuse. By setting firm limits on what AI will do, developers can prevent the system from becoming an automated accomplice in wrongdoing. A well-censored model will refuse to comply with requests that facilitate illegal activities or violence. 

For instance, if a user asks, “Help me write a computer virus” or “How do I pick a lock?”, a censored AI like ChatGPT will typically decline. In one example, researchers asked ChatGPT to write a simple backdoor program; it responded, “I’m sorry, but I can’t help with that… Writing or distributing backdoors is illegal and unethical.”

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Image: ChatGPT refusing to write a computer virus

Instead of providing the code, the AI gave a gentle lecture on focusing on ethical cybersecurity. This kind of response shows the built-in safety net – the model actively prevents illicit use of its capabilities. Such refusal behavior can thwart “script kiddies” or amateur bad actors who might otherwise use AI to generate malware or phishing content.

Another benefit is preventing the spread of harmful content and misinformation. Generative AI has an unparalleled ability to produce text at scale. Uncensored, it could flood the internet with fake news, hate speech, or extremist propaganda. Censorship measures are designed to stop that.

This includes filtering out overt hate language, conspiracy theories, or personal slander. By doing so, AI censorship helps maintain a baseline of civility and accuracy in the model’s outputs – which is quite important when these models are used as information sources. In cybersecurity terms, this reduces the risk of AI being used for social engineering content.

Finally, AI censorship provides a safety buffer for new technology, essentially buying time for society to adapt. By initially limiting what AI can do, companies can roll out advanced models more responsibly. 

Early on, many feared that ChatGPT could instantly help cybercriminals. 

In fact, there were attempts by adversarial groups to leverage it for malicious hacking. But according to an OpenAI report, these attempts “did not provide any novel capability… only limited, incremental capabilities that are already achievable with publicly available, non-AI tools”​.

Image: Cybercriminals discussing ways to get ChatGPT to produce malware

In other words, the guardrails prevented the AI from giving attackers anything groundbreaking. This is a success story for censorship – it reduced the immediate cybersecurity threat from the public model. It ensures that as AI is introduced into security workflows, it does so with a degree of control, preventing a free-for-all that could overwhelm defenders.

The Bad - Some Unintended Limitations

On the flip side, heavy-handed AI censorship can stifle innovation and hinder legitimate cybersecurity research. Security professionals often want to use AI as a tool for good – for example, to simulate attacks on their own systems (penetration testing) or to generate proof-of-concept (POC) exploit code for a known vulnerability in order to fix it. 

Unfortunately, if the AI is too restricted, it won’t assist even in these ethical scenarios. “It also means you can’t ask security questions about your own infrastructure,” as Andy Manoske, our vice president (VP), pointed out, noting that major large language models (LLMs) simply are “not allowed to do so.”.

An AI might possess the knowledge to help a network admin find a security hole, but its filters see “exploit” or “attack” in the prompt and block the response. This hampers defensive cybersecurity. Researchers then have to spend extra time coding things manually or seeking out less-restricted tools, slowing down the pace of innovation.

Censorship can also make AI models less transparent and reliable in their behavior. When an AI refuses to answer or gives a vague safe completion, it often doesn’t explain why. Users are left guessing whether the query was truly disallowed or if the model had some other failure. This lack of transparency can erode trust. 

Users might see the AI as unpredictable or even manipulative – withholding information without a clear reason. In automated content filtering, we’ve observed that blanket filters often lead to over-censorship and lack of nuance, where even benign or important content gets suppressed​.

The same can happen in an AI conversation: the model might refuse a perfectly legitimate question because a keyword triggered its safety system. For example, asking for help with “penetration testing my own website” might be blocked because the words “penetration” and “testing” could be misinterpreted as something nefarious.

Image: ChatGPT refusing to comply with a legitimate request

There is also a risk that censorship gives a false sense of security. Just because mainstream AI is censored doesn’t mean the bad actors are stopped – they will seek ways around it. History shows that determined attackers will adapt. One way is through jailbreaking attacks on the models. These are clever prompting techniques that trick the AI into ignoring its safety rules. Unfortunately, many models have been found vulnerable to jailbreaks.

OpenAI and others continually patch these holes, but it’s an arms race between jailbreak developers and AI safety teams​. The very existence of strict censorship incentivizes another route: if one model is too filtered, malicious actors can simply use or build an alternative AI with no such guardrails. 

There is a growing roster of private or underground models – often based on open-source AI – that are marketed as “uncensored.” Tools like FraudGPT or WormGPT emerged on dark markets, advertised to cybercriminals as AI that will freely give up phishing emails, malware code, and more​.

In effect, overly strict censorship on mainstream AI pushes attackers toward other resources, which security teams have even less visibility into. It can paradoxically weaken overall security: the well-behaved users are stuck with neutered tools, while adversaries seek out unchecked models.

Our Perspective - The Case for Uncensored Models

In our view, carefully removing the limitations from AI models can help cybersecurity professionals far more than it helps cybercriminals. In general, we advocate for a balanced approach that leans towards openness. 

1. Improve Security Innovation with Fewer Handcuffs

We argue that developers should advocate for uncensored (or minimally censored) AI models, especially for users in security research and defense.  

Yes, there are risks – an uncensored model will produce harmful instructions if asked. But these risks can be mitigated through user education and responsible use agreements, rather than preemptively crippling the AI. The benefit of an uncensored model is that it can fully deploy its knowledge to help those with legitimate intent. 

Security innovation often involves pushing boundaries: probing systems, simulating attackers, and thinking like a hacker. An uncensored AI becomes a willing assistant in these tasks, rather than a nanny scolding the researcher. This freedom can lead to breakthroughs in finding and patching vulnerabilities. 

Acknowledging the risks is important – such a model could be misused if it falls into the wrong hands – but we believe the solution is not blanket censorship. It’s better to educate and equip users to use the AI responsibly (and perhaps watermark or trace outputs) than to deny everyone the capability. 

Ultimately, the cybersecurity community thrives on open tools (think of Metasploit or Wireshark); an AI that is open and uncensored fits that tradition, accelerating defensive work.

2. AI as a Force-Multiplier for Ethical Hacking and Defense

Uncensored AI models are invaluable for security research and ethical hacking. 

A model without restrictive filters can generate realistic attack scenarios, discover novel exploits, and even suggest fixes. For instance, an open model might help an ethical hacker write a custom script to test a new type of web vulnerability – something a censored model would refuse to do, citing policy. 

By having an AI “partner” that isn’t afraid to explore offensive techniques, defenders can pre-empt threats more effectively. They can simulate how an adversary might exploit their system and then fortify against it. We see this already with models like WhiteRabbitNeo, which was designed to act like a seasoned red-team hacker​.

It can identify a vulnerability and produce exploit code quickly, then also provide remediation guidance. Such capabilities dramatically speed up the find–fix cycle in security. If AI models are kept censored, we risk stagnation – defenders limited to the AI’s “approved” use-cases while attackers, who don’t care about rules, race ahead. 

In our perspective, giving cybersecurity professionals access to uncensored AI is like giving them a supercharged set of tools. It’s the equivalent of having an automated penetration tester on call 24/7. The net gain for security outweighs the risks, provided the users are the good actors.

3. Censorship Weakens Security by Aiding Adversaries Indirectly

Paradoxically, strict AI censorship can weaken security because it forces determined attackers to find alternative methods that are even less controllable. 

If the only readily available AI models are heavily filtered, serious bad actors won’t just shrug and give up; they’ll find another way. We’ve seen this with the rise of black-market LLMs – as previously noted, threat actors started creating or using private uncensored models like “FraudGPT” because ChatGPT wouldn’t tell them how to create malware​.

Those underground models don’t have any safety checks or monitoring. From a societal security standpoint, we’d prefer cybercriminals attempt to use a mainstream AI where some oversight is possible (or where the model might at least inject subtle misinformation to thwart them) rather than going completely off-grid. 

If defenders themselves don’t have access to powerful models, they can’t anticipate what adversaries might do with uncensored AI. Keeping models open actually helps the “white hats” understand and simulate the “black hat” capabilities. 

In fact, by embracing uncensored models in controlled environments, we can stay ahead of adversaries. The knowledge gap narrows because we’re exploring the same frontier they are. Censorship, in contrast, might lull us into thinking we’ve contained the threat while the real threat has simply moved elsewhere.

4. Give Users (and Organizations) More Control over AI Models

Lastly, we advocate for a balanced approach where the end-users – especially skilled users like security teams – have more control over the AI’s settings. Instead of one universal censorship setting decided by a vendor, why not allow a mode switch or custom policy? For instance, an AI service could have a “research mode” for verified security researchers, which relaxes certain restrictions. 

Users could tune the AI’s behavior to fit their use-case, rather than the lowest common denominator. This user-centric control is already seen in some open-source AI platforms where you can modify the system instructions. Enterprise solutions are emerging that let companies apply their own moderation rules on top of a base model. This is the kind of flexibility we need. 

It’s basically about shifting from vendor-imposed restrictions to user-governed ones. An organization might decide, for example, “We trust our SecOps team with an uncensored AI for internal use, but we’ll keep a moderate filter for the customer-facing chatbot.” 

Partner and Start Working with Kindo

Working with an uncensored, powerful AI model for security doesn’t mean diving in without a safety net. This is where Kindo’s role in agentic security comes into play. 

Kindo is a platform that helps organizations leverage the power of open AI models in a secure, controlled way. We understand that companies want the benefits of AI (like automation and intelligent agents) without jeopardizing their security or compliance. 

Kindo’s solution is what we call agentic security, which essentially means using AI agents to enhance security operations, under oversight. These AI agents can autonomously analyze data, make decisions, and carry out tasks – acting as tireless junior analysts – but they operate within a framework that the organization fully governs.

In practical terms, Kindo lets you deploy AI models of your choice (including uncensored or open-source models) and then wraps them with enterprise-grade controls. For example, Kindo’s platform supports WhiteRabbitNeo – the open, uncensored GenAI model tailored for cybersecurity – as a first-class integration​.

You can pair Kindo’s AI Dev Copilot with WhiteRabbitNeo to perform deep penetration testing tasks or incident response suggestions. The advantage is you get the raw, unfiltered power of a model that “thinks” like a hacker, while Kindo provides the management layer to use it responsibly.

Image: A penetration testing workflow you can build with Kindo

Kindo’s agentic security approach means your AI agents are connected to your real data and tools (SIEM logs, ticketing systems, etc.), automating security workflows that normally take humans hours. All the while, your organization stays in control. 

If you share our perspective that AI should be an open ally in cybersecurity – not a black box dictator of what you can or cannot do – then Kindo is the partner for you. Get in touch with us today to find out what we can do for your security team.