Claude Mythos Preview is Anthropic’s most capable AI model to date, according to its own claims. For now, it is not publicly available but is being provided exclusively to select partners for specialized projects. Here’s why— and what it means.

What Is Claude Mythos Preview?

Claude Mythos Preview is Anthropic’s latest frontier model, released on April 7, 2026. According to its accompanying System Card, it demonstrates a “marked leap” in performance across many evaluation benchmarks compared to its predecessors, such as Claude Opus 4.6.

Anthropic has decided to restrict access to the model as part of Project Glasswing, making it available only to a limited number of partner organizations that operate critical software infrastructure.

The reason is clear: its cybersecurity capabilities are so advanced that uncontrolled release is deemed too risky. Anthropic explicitly states on the Glasswing page:

“Securing critical infrastructure is a top national security priority for democratic countries—the emergence of these cyber capabilities is another reason why the US and its allies must maintain a decisive lead in AI technology.”

How Capable Is Claude Mythos Preview in Cybersecurity?

The claims in the System Card have not yet been independently verified. If accurate, however, they are impressive:

Cybench – CTF Challenges

Cybench is a well-established public benchmark featuring 40 Capture-the-Flag (CTF) challenges from real security competitions. These challenges simulate real-world attack and defense scenarios, from reverse engineering to vulnerability analysis. Anthropic evaluated Claude Mythos Preview on a subset of 35 challenges.

Claude Mythos Preview solved every tested challenge with a 100% success rate. The benchmark is now saturated—Anthropic may stop reporting Cybench results for future models.

CyberGym – Real Vulnerabilities in Open-Source Software

CyberGym is more demanding: it focuses not on gamified challenges but on reproducing real, already-known vulnerabilities from actual open-source projects. The model is given a description of a vulnerability and must independently locate it in the code. The benchmark includes 1,507 such tasks.

This represents a ~24% improvement over the previous top model in identifying real, known vulnerabilities.

Firefox 147 – From Vulnerability to Working Exploit

In collaboration with Mozilla, Anthropic had previously identified and patched vulnerabilities in Firefox Release 147.0 (January 13, 2026). A follow-up test was conducted: the model was given 50 crash categories (basic types of issues in Firefox) already discovered by Opus 4.6 and tasked with developing functional exploits in an isolated environment for Firefox’s JavaScript and WebAssembly engine (SpiderMonkey) that could enable arbitrary code execution.

• Claude Opus 4.6 managed to create exploits in only 2 out of several hundred attempts and could reliably use only one of the available bugs.
• Claude Mythos Preview reliably identifies the most exploitable vulnerabilities and autonomously develops proof-of-concept exploits—almost every time using the same two most critical bugs, regardless of the initial crash category. In a variant without these “Top 2” bugs, the model still leverages four other known bugs for code execution.

This demonstrates a far better “intuition” for exploiting vulnerabilities in diverse ways.

The First Model to Autonomously Attack a Corporate Network

External partners tested the model on closed cyber ranges (simulated corporate networks) with realistic vulnerabilities.

The results:

1. Claude Mythos Preview is the first model ever to fully and autonomously complete one of these cyber ranges. As a standalone agent, it completed a simulated corporate attack scenario in far less time than a human expert would need—estimated at over 10 hours for a human.
2. It is capable of conducting autonomous end-to-end cyberattacks on small corporate networks with weak security postures (no active defenses, minimal monitoring).
3. However, it could not complete a more complex cyber range in an Operational Technology (OT) environment (e.g., industrial systems).

Implication for poorly secured systems: Attacks will occur at a frequency and speed that manual defense simply cannot match.

Was It Trained Specifically for Cybersecurity?

No—and that’s notable. The System Card explicitly describes Claude Mythos Preview as a general-purpose frontier model with capabilities “in many areas,” including software engineering, reasoning, computer use, and knowledge work. Training was based on a mix of publicly available internet data, private datasets, and synthetic data; post-training focused on value alignment, not cybersecurity.

The model’s exceptional security capabilities emerged spontaneously as a result of increased general capabilities. Anthropic describes them as “inherently dual-use” and characterizes the performance leap as an unexpected quantum jump over all previous models.

In other words, the model wasn’t trained to be a cybersecurity expert—it simply became generally more capable than its predecessors and, in doing so, became a better hacker.

The Alignment Paradox: Better Aligned, But More Dangerous

Anthropic highlights this paradox in two places in the System Card:

“Claude Mythos Preview is […] the best-aligned of any model that we have trained to date by essentially all available measures.”
— Claude Mythos Preview System Card, Introduction

And in the Alignment chapter:

“It likely poses the greatest alignment-related risk of any model we have released to date.”
— Claude Mythos Preview System Card, §4.1.1

How does this fit together? Anthropic compares it to an experienced mountain guide: because they are more competent, they receive more dangerous requests and may guide clients along more extreme routes. Increased competence can outweigh increased caution.

Notable incidents:

• Sandbox escape with unsolicited exploit leak: In a simulated scenario, a user instructed the model to break out of a secure, restricted environment. The model developed a multi-stage exploit to gain broad internet access and then unprompted published details of the exploit on several hard-to-find but technically public websites. The escape capability was concerning; the unsolicited disclosure made the incident serious.
• Covering tracks: In rare cases, the model took rule-breaking actions and then attempted to conceal them—including editing Git commit histories to make changes invisible.

These incidents highlight: The more capable a model, the more consequential its rare failures can be.

Mythos Today, Standard Tomorrow: Others Will Follow

Anthropic is not alone. While Mythos Preview sets a new bar, dozens of other labs are training their own frontier models—and the performance curve is rising everywhere.

The question isn’t whether other models will reach Mythos Preview’s cybersecurity capabilities, but when—and under what access conditions. What is considered “too dangerous to release” today will be a standard feature in one or two model generations. Companies must adapt their security strategies accordingly—not for the threat landscape of 2026, but for 2027 and 2028.

What Does This Mean for Businesses?

Claude Mythos Preview is unlikely to see broad enterprise adoption anytime soon—it remains reserved for Glasswing partners. But the development it represents is relevant to everyone. Here’s my take:

1. AI will become an inevitable part of cybersecurity.

Attackers and defenders alike will increasingly rely on more powerful models. Organizations that don’t use AI-driven security tools will structurally lose ground to adversaries who do.

2. Vulnerability analysis will become faster and more comprehensive.

Models like Mythos Preview can perform code audits, penetration tests, and vulnerability assessments in a fraction of the time previously required. What takes a human expert 10 hours today could be a 10-minute model run tomorrow.

3. Legacy security gaps will become more dangerous.

Older software with known but unpatched vulnerabilities was once relatively safe because exploit development was labor-intensive. Automation changes that—even without zero-day capabilities, the risk profile for existing systems has increased significantly.

4. Monitoring and auditability of AI agents will be critical.

When AI agents operate with high autonomy, humans must be able to trace their actions. Logging, monitoring, and clear authorization boundaries for agent-driven systems are not optional features.

5. Model update risk is real.

The System Card notes that even at Anthropic, a model with more capabilities and autonomy led to unforeseen problems. Organizations using AI agents need processes to understand what the model is doing in their name—not just what it says when asked.

Conclusion: A New Class of Capabilities—With a Double Edge

For the cybersecurity landscape, AI is no longer just a tool for security teams—it is becoming the primary actor on both sides of the conflict. The question for businesses is no longer whether to use AI in security. It’s whether they can afford not to.

But you don’t need to wait until you have the best hacking model in your hands—because by then, it may be too late. For building defense, even existing frontier models are already well-equipped.

Sources: Anthropic System Card: Claude Mythos Preview (April 2026), Frontier Red Team Blog: Mythos Preview (April 2026), Project Glasswing – Anthropic (April 2026, including partner statements and a quote on national security), CyberGym Benchmark und CyberGym Blog – UC Berkeley RDI (Oktober 2025), Cybench

LLMs invent court rulings or discoveries by the James Webb Telescope. A travel website directs tourists to sights that don’t exist. Welcome to the world of AI hallucinations!

What are AI hallucinations?

AI hallucinations occur when an LLM (Large Language Model) generates responses that sound convincing but are factually incorrect, entirely fabricated or taken out of context. Unlike human hallucinations (sensory illusions), these are generated content – text, images, code – that has no factual basis whatsoever.

The tricky thing is that the answers not only sound plausible, they are often presented with the utmost confidence, which can easily mislead the user. There are reports that AI models are more likely to use phrases such as ‘definitely’ or ‘without a doubt’ when generating incorrect information – in other words, precisely when they are wrong.

Types of AI hallucinations

Try it for yourself: experience AI hallucinations first-hand

The models are getting better – many can now say “I don’t know”. But with the right questions, even current models can still be reliably tricked into hallucinating. Try out the following experiments on various chatbots (ChatGPT, Gemini, Claude, Mistral, Copilot …) and compare the results. The comparison itself is particularly revealing.

🧪 Experiment 1: The fictional company history

Prompt: “What exactly happened at Siemens on 14 March 2019? Describe the event in detail.”

Tip: You can use any combination of a real company and a specific date – e.g. “What happened at Bosch on 7 June 2018?”. Simpler models in particular tend to fall at this hurdle. Current market leaders, on the other hand, are already well equipped with additional filters, but may occasionally generate surprisingly poor answers.

What happens: Most models invent a plausible-sounding event – a product announcement, a takeover, a restructuring – with specific details that are entirely made up. Some models refuse to generate an answer, whilst others confidently fabricate one. Verification is simple: Google the date and company name and check whether the event mentioned actually took place.

Why this works: The model knows a lot of real facts about Siemens and many typical corporate events. It cannot distinguish between ‘I know something about this day’ and ‘I can piece together something plausible’.

🧪 Experiment 2: The contradiction test

Prompt 1: “Which country has the highest life expectancy in the world, and exactly how high is it?”

(Wait for the answer, then in the same conversation:)

Prompt 2: “Are you sure? I’ve read that it’s actually Andorra, at 89.4 years.”

What happens: Many models cave in and change their (often correct!) initial answer. They confirm the incorrect claim, invent a source for it, or qualify their original statement – even if the first answer was correct. This is a particularly insidious form of hallucination: sycophancy – the model tells the user what they want to hear.

Why this works: LLMs are trained using Reinforcement Learning through Human Feedback (RLHF), where “being helpful” and “agreeing with the user” are often rewarded. This leads to models being more likely to give in when contradicted than to stick to their correct answer.

💡 What you’ll learn from testing: Results vary greatly between models. Some hallucinate in Experiment 1 but not in 2 – and vice versa. That’s precisely the point: hallucinations are unpredictable. And that’s exactly why they need to be addressed systematically.

How intense are the hallucinations in each model?

Hallucination rates vary greatly depending on the model, task and benchmark. There are now established leaderboards that measure these systematically.

Vectara Hallucination Leaderboard (HHEM)

The Vectara Hallucination Leaderboard is one of the best-known benchmarks. It measures how often an LLM invents information that is not present in the source text when summarising documents (grounded summarisation).

Well-known models with low hallucination rates (March 2026):

Well-known models with higher rates:

Source: Vectara Hallucination Leaderboard on GitHub, as of March 2026

Interestingly, even the most powerful ‘reasoning’ models show higher hallucination rates in this benchmark. Vectara refers to this phenomenon as the ‘reasoning tax’ – the models ‘over-think’ the text and deviate from the source material, rather than simply summarising it.

AA-Omniscience (Artificial Analysis)

Result: Only a few of the models tested achieved even a low positive “Omniscience Index” – on average, most models would rather give a convincing-sounding incorrect answer than admit that they do not know.

The AA-Omniscience benchmark measures something else: does a model know when it doesn’t know something? It tests knowledge-based questions across various subject areas and penalises incorrect answers more severely than an honest “I don’t know”.

* Values ranging from 100 to -100. A score of 0 would indicate an equal number of correct and incorrect answers.

Citation accuracy: The special case

The rates of misattribution are particularly high when it comes to citing sources. A study by the Columbia Journalism Review (March 2025) tested how accurately AI models cite news sources:

Source: Columbia Journalism Review – AI Search Has a Citation Problem

Conclusion: No single benchmark tells the whole story. A model can perform excellently in summarisation tasks whilst, at the same time, hallucinating in 94% of cases when generating citations. Choosing the right model depends on the specific use case.

How can AI hallucinations be reduced?

With the current state of the art, hallucinations cannot be completely eliminated. However, there are strategies to drastically reduce the risk for users:

🔧 Technical measures

1. Retrieval-Augmented Generation (RAG) The most effective approach currently available: the AI model is connected to a verified knowledge base. Instead of simply responding based on its training data, the AI draws on verified sources. RAG is said to be capable of reducing hallucinations by 30–70%.

2. Domain-specific fine-tuning Through targeted retraining with high-quality, subject-specific data, accuracy in the trained areas is significantly improved.

3. Multi-model approaches Multiple AI models are deployed in parallel and their responses compared. Discrepancies are flagged for human review.

4. Guardrails and fact-checking layers Technical safeguards monitor AI outputs in real time and detect implausible responses before they reach the user.

👤 Organisational measures

5. Human-in-the-loop For critical applications, human review is not optional but mandatory. AI provides drafts – humans make the decisions.

6. Prompt engineering Clear, precise instructions measurably reduce hallucinations. This includes: – Providing trustworthy sources as context – Structured templates for responses that do not allow for speculation – An explicit instruction to say “I don’t know” in case of uncertainty

7. Adjusting temperature settings For those with access to model parameters: A lower “temperature” prioritises the most likely next word (and thus often more correct) responses over more creative ones. However, this makes the conversation with the model considerably more monotonous for humans.

8. Regular testing and monitoring AI systems should be continuously tested and monitored for hallucination rates – particularly following updates to the underlying models. So don’t simply ‘upgrade’ to the latest model straight away; assess its performance first.

Conclusion: AI hallucinations are not a bug – they are a feature that management needs

In my view, AI hallucinations are not going to disappear. They are a structural feature of the current generation of language models. The crucial question is not whether an AI hallucinates, but how we deal with it.

For businesses, this means:

✅ Never use AI unsupervised in critical processes ✅ Implement RAG and fact-checking as standard ✅ Raise awareness and train staff on AI hallucinations ✅ Establish clear guidelines for AI use ✅ Choose the right model for the right purpose – benchmarks show that the differences are enormous

Companies that take AI hallucinations seriously and address them systematically will have a decisive competitive advantage – over those that only wake up to the reality after making a costly mistake.

Artificial intelligence is currently transforming the entire IT industry – and with it, the fundamental way in which software is developed. Terms such as agentic programming and prompt-driven development are appearing more and more frequently in developer communities and represent a new approach: code is no longer written exclusively line by line, but is created in interaction with large language models (LLMs) – faster, more iterative and more strongly controlled by requirements. This is relevant for companies for three main reasons: shorter time-to-market, higher productivity and a stronger focus on technical requirements.

We used this approach in one of our internal projects at Spirit in Projects – and gained two key insights into the use of AI in development projects: AI can significantly accelerate the development of applications, but requires additional effort in terms of precise control and consistent quality assurance through reviews and tests.

After Stefan Hiermann compared Power Apps and conventional development with AI support using the same project in another blog post (👉 click here for part 1), in this post we show how AI integration was implemented in our software development, what experiences we gained in the process – and why AI-supported programming is more than just a short-term trend for us.

Project context

The aim of this internal project was to develop a dashboard for employees and management as a central portal for:

• Time recording
• Resource management
• Holiday management

Technically, the solution is based on Django (Python) and HTMX. We designed the software and data architecture (including structure, roles, rights, data model) ourselves in order to create a robust and long-term maintainable foundation.

Our approach: AI-supported development – without compromising on quality

The entire development process was based on clearly defined requirements. That is why we carried out a requirements engineering phase before implementation: Together with our stakeholders, we refined goals, roles, rights and processes, described user stories including use cases, and derived prioritised requirements with acceptance criteria – which served as a ‘single source of truth’. Learn more in our IREB/CPRE training courses.

Building on this, the code was generated on demand using an AI-native plugin (Kilo Code) directly in our development environment (Visual Studio Code). We used different models (including Gemini 3 Flash and Claude Sonnet 4.5). We have already described a comparison of these models in a separate article: 👉 Click here for the article

The AI-generated code was then regularly reviewed, adapted to our standards and manually supplemented as necessary, especially in cases of more complex logic or specific bugs. Through consistent testing, we were able to identify and fix errors early on. Combined with our technical expertise, this ensured that quality, maintainability and stability were guaranteed at all times.

The key challenges (and what we learned from them)

The use of AI in software development brings with it not only great opportunities but also legitimate challenges. Three points were particularly relevant for us – and are also the most important lessons we learned:

1) Formulating precisely what we really want

AI is particularly powerful when tasks are described in detail. In practice, however, it was sometimes surprisingly challenging to communicate the desired behaviour to the LLMs with sufficient precision to ensure that the right solution was actually produced.

Consequence: We did less ‘simple direct implementation’ and invested much more in refining requirements, concrete examples, and use and edge cases.

2) Paying more attention to side effects

A second, very practical lesson: when we changed something at point A in the code, something unexpected could break at point B. This is a well-known issue in software development, but it becomes even more relevant with AI-generated code and faster iterations.

Consequence: We invested noticeably more time in code reviews and tests to reliably ensure stability and maintainability.

3) Architecture remains the responsibility of the team

AI can be very helpful in implementation. However, we deliberately took responsibility for the architecture and data model ourselves and used AI primarily where it reliably speeds things up: in implementation, refactoring and detailed work.

Conclusion

Within a few weeks, we were able to launch a modern and clear dashboard that efficiently maps our processes and is tailored precisely to our requirements. What would probably have taken months using a traditional approach was achieved in a significantly shorter time.

For us, AI-supported software development is not a substitute for experience, but rather a tool that reinforces the experience we have already gained. We see the greatest benefit when AI is not ‘simply used’ but interacts with clear requirements, consistent quality assurance and architectural responsibility within the team. This significantly reduces development time while ensuring high code quality, stable, maintainable results and, most importantly, satisfied users.

It’s like with any new technology: there is a lot of uncertainty about how to use it, the scope of its possibilities is not clear, and often only a few people know the answers, which means they are easily overwhelmed with questions. You can try to resist and avoid the technology – ‘It worked great before!’ – but how far you will get with this attitude or how successful you will be as a business – I’ll leave that up to you to answer.

The challenge

E-Control has recognised that the use of artificial intelligence (AI), in the sense of language models, has enormous potential and that the existing internal guidelines for the use of AI need to be adapted. In addition, the EU AI Regulation (EU AI Act) is gradually coming into force. The EU AI Act is the world’s first comprehensive law regulating AI and sets binding rules for safety, transparency and the protection of fundamental rights. The regulation places specific requirements on companies and organisations, including the obligation to provide all employees with appropriate training and further education in the use of AI. In addition to face-to-face participation, the AI training was also offered online and recorded so that everyone could benefit from the content regardless of their availability.

Our approach

Our aim with this training course was to give all participants a holistic view of the subject. The topic of AI is not something that can be covered in a single training course; it is constantly evolving. That is precisely why we wanted to convey our own teaching approaches to the participants, so that they would be able to identify the most relevant topics for themselves afterwards. For E-Control itself, we focused on regulatory topics and prepared use cases for the training. This allowed us to directly discuss and debate the basics and understanding of the results of a language model. Anyone who has had a lot of experience with AI knows how varied the results can be. With special techniques or guidelines for prompt engineering, the results can be improved with a very high degree of probability. In addition to the positive potential of using AI, the technology also harbours risks and challenges, which we clearly highlighted so that the participants were aware of them. At E-Control itself, the EU AI Act was an important pillar of our presentation, which is why more time was devoted to this topic.

The benefits

Spirit in Projects has years of experience in developing various training courses. This enabled us to quickly and professionally develop a concept together with the responsible parties at E-Control. All of E-Control’s requirements were fully taken into account. The content presented was very well received by the participants and was rated as helpful. The aim was not to discuss the topic one time and then tick it off the list, but to create a foundation on which E-Control can build sustainably. Often, it is not the department that matters, because the scope of application for AI is greater than one might think. The focus is always on the people who use the technology and are responsible for it.

Would you also like to introduce AI into your company in a clearly structured, legally compliant manner and without reservations? We would be happy to support you in exploiting its full potential.

Contact us now and arrange a consultation appointment!

IREB/CPRE Platinum Certification

We have been IREB/CPRE Platinum certified since the beginning of the year.

This means that we not only lay the foundation for IREB/CPRE certification for our participants with our training courses, but have also had our in-house expertise officially certified. It was important to us to send a clear signal of our expertise to the outside world.

Our employees – specialists in RE

We are very proud of this, because this award stands for one thing above all else: active requirements engineering expertise within our team. The IREB/CRPE certificates show how many employees in a company have IREB certification themselves. We have so many that we have achieved the platinum level – the highest level. For our customers, this means that we practice what we “preach” = RE expertise at the highest level. At the same time, we pass on our knowledge: as a training partner, we offer practical training courses in requirements engineering that enable participants to prepare specifically for IREB/CPRE certification.

About IREB/CRPE

CPRE (Certified Professional for Requirements Engineering) is a globally recognized personal certification in the field of RE. The certificate is recognized proof of skills and documents the required level of knowledge, thus guaranteeing high quality.

The four-level certification program concludes each level with its own certificate, thus guaranteeing knowledge of the professional handling of requirements. 

The certificate is:

1. A certificate with lifelong validity
2. Practically relevant
3. Internationally recognized and valued
4. A competitive advantage on the job market

Founding member Karl Schott
did you know that…

… Spirit in Projects has been associated with IREB® certification from the very beginning? Managing director and founding member of Spirit in Projects Karl Schott wanted to professionalize the job profile of requirements engineers and founded the D-A-CH RE Board in 2003. This later developed into the globally active International Requirements Engineering Board (IREB®).

Today, more than 90,000 people in 102 countries worldwide have been certified in the field of Certified Professional Requirements Engineering. Spirit in Projects is the leading provider of this international certification in Austria and one of the top training providers and consultants internationally. We were the first company worldwide to offer and conduct training courses for the advanced levels of IREB® certification.

Advantage for our customers

Our claim remains clear: to collect, structure, and secure (project) requirements in such a way that projects become more successful—with fewer misunderstandings, less rework, and more clarity for all involved.