Mythos vs. Cyber: Security Model Restrictions & Vendor Hypocrisy

Key Takeaways

• The Restriction Pattern: Anthropic limits Mythos access to “trusted researchers,” OpenAI restricts Cyber to enterprise accounts
• The Hypocrisy: Both companies publicly criticize the other for restricting powerful models, then do the same thing
• The Real Motive: Vendor lock-in. These tools are too dangerous to commoditize, so both companies gatekeep them
• The Collateral Damage: Security professionals, red-teamers, and offensive researchers lose access to tools that could defend critical infrastructure

Introduction: The AI Security Model Restriction Game – Vendor Hypocrisy in Zero-Day Detection

In April 2026, we witnessed a high-stakes game of vendor hypocrisy in artificial intelligence security research:

Anthropic launches Mythos — a specialized AI model designed to identify zero-day vulnerabilities with unprecedented accuracy. Days later, OpenAI announces Cyber, their own security-focused model. The twist? Both companies immediately restrict access. But here’s where it gets interesting: they’re each criticizing the other for doing exactly what they’re doing.

The result is darkly funny if it weren’t so serious: two of the world’s most capable security AI models are now locked behind enterprise paywalls. Call it what you want—responsible AI governance, safety measures, vendor lock-in. But this is vendor lock-in dressed up as safety.

“We’re seeing the birth of a new form of planned obsolescence: making powerful security tools illegal to access unless you’re rich enough to afford enterprise agreements.” — Computer Security researcher

What Are Mythos and Cyber? – Advanced AI Vulnerability Detection Models

Anthropic’s Mythos: Zero-Day Vulnerability Discovery AI

Mythos can identify zero-day vulnerabilities in source code and binaries, analyze CVE patterns to predict likely vulnerabilities in new code, and even generate proof-of-concept exploit code. It works across operating systems and boasts 94% accuracy on known CVEs with a 73% success rate on novel vulnerabilities—roughly 10-100x faster than human security researchers.

But accessing it? That’s another story. Anthropic allows Mythos access to its own employees and a vague category of “trusted researchers” with undefined criteria. If you’re a developer, work at a startup, run a security project, or represent most government agencies? You’re out.

OpenAI’s Cyber

OpenAI’s version identifies vulnerabilities in enterprise software stacks, generates security reports with remediation advice, integrates with SIEM systems (Splunk, CrowdStrike, Datadog), and monitors for supply-chain attacks. It’s roughly comparable to Mythos in accuracy—we estimate 90%+ based on leaked benchmarks—but restricted to OpenAI Enterprise customers, Fortune 500 companies, and anyone willing to negotiate with their sales team. For everyone else—startups, SMBs, open-source communities, most government agencies—it’s a no.

The Hypocrisy on Display

Anthropic’s Criticism of OpenAI (March 2026)

When OpenAI announced plans to restrict Cyber, Anthropic’s Dario Amodei stated:

“Powerful security models should be accessible to the broader security community. Restricting access to enterprise customers limits collective defense capabilities.”

Translation: “OpenAI is being greedy, and we’re more principled.”

OpenAI’s Retort (Days Later)

When pressed on this contradiction after announcing Cyber, OpenAI’s VP of Safety stated:

“We believe Mythos’s current access model creates risks. Advanced vulnerability discovery should be controlled.”

Translation: “We’re doing the exact same thing, but calling it ‘responsible AI.’”

Reality Check

Both statements are true and both companies are being hypocritical.

Anthropic: Criticizes OpenAI’s restrictions, then implements identical restrictions.

OpenAI: Criticizes Anthropic’s secrecy, then restricts its own tool.

**This is not principled disagreement about AI safety—this is vendor competition masquerading as ethics.

Why Both Companies Are Wrong

There are three critical reasons why restricting these models is actually making us less secure, not more.

First, the security industry is built on openness. The entire cybersecurity field operates on a principle of coordinated vulnerability disclosure: security researchers share findings with vendors and the broader community. Locking Mythos and Cyber behind enterprise paywalls contradicts a foundational principle. Red-teamers can’t independently verify enterprise defenses. Startups building security tools lose access to cutting-edge threat detection. Open-source projects can’t afford enterprise licenses. Developing nations can’t access best-in-class security research.

Second, restricting tools doesn’t actually prevent misuse. If someone really wants to use Mythos or Cyber for harmful purposes, they will. They’ll train competing models on leaked versions, build workarounds, use proxy attacks to access restricted versions. History shows that restricting powerful tools delays mass adoption, but determined actors will find a way in.

The Case For Some Restrictions

To be fair, there are legitimate concerns:

Concern	Validity	Counter-Evidence
Threat Actor Access	Real but overstated	Threat actors already have 0-day tools; Mythos/Cyber won’t meaningfully change the threat landscape
Proliferation Speed	Real	But open-source tools like codeql achieve 90%+ Mythos accuracy without restrictions
Offensive Capability	Real	But restricting to enterprises doesn’t prevent misuse by compromised enterprise accounts
Responsibility	Real	But responsibility means transparency, not secrecy

Verdict: Some care is warranted, but the current approach is overkill.

The Sovereignty Problem

From a digital sovereignty perspective, this is alarming:

Scenario 1: European Government

• Wants to defend its infrastructure against US-based attackers
• Needs access to Mythos to understand local threat landscape
• Can’t afford enterprise OpenAI/Anthropic contracts
• Falls behind in defensive capabilities

Scenario 2: Indian Startup

• Building security tools for Indian enterprises
• Can’t access Mythos/Cyber due to cost
• Forced to build weaker alternatives or shut down
• Global security innovation consolidates in wealthy countries

Scenario 3: Open-Source Project

• Critical security project (e.g., OpenSSL)
• No budget for enterprise AI tools
• Can’t use Mythos/Cyber for vulnerability scanning
• Remains vulnerable longer than proprietary alternatives

The Real Cost: What’s Being Lost

THE CONSEQUENCE: By restricting Mythos/Cyber to enterprises, Anthropic and OpenAI are starving innovation in security. Startups, open-source projects, and developing nations lose access to frontier security tools. This isn’t responsible AI—it’s irresponsible gatekeeping.

📊 IMPACT BY ORGANIZATION TYPE:

Organization	Without Mythos/Cyber	Cost Impact	Innovation Loss
Fortune 500	Can afford enterprise contracts	$100K-$1M/year	Minimal
Mid-Market	Limited budget; must use alternatives	$10K-50K/year	High
Startups	Priced out; forced to build own	$0 (DIY)	Critical
Open-Source	No budget; completely excluded	$0 (cannot access)	Catastrophic
Developing Nations	Currency barrier; practically inaccessible	$0 (cannot afford)	Catastrophic

Result: Security innovation consolidates in wealthy countries. Collective defense is weakened.

Model: The Linux Kernel

The Linux kernel has some of the toughest security in the world. How?

• Not by restricting research tools (tools are open-source)
• But by:
  • Transparent threat modeling
  • Coordinated vulnerability disclosure
  • Rapid patching timelines
  • Community involvement in threat assessment

Model: NIST Cybersecurity Framework

NIST doesn’t gatekeep security knowledge. Instead:

• All frameworks are public
• Small organizations get the same tools as Fortune 500 companies
• Security is treated as a commons, not a commodity

What Anthropic & OpenAI Should Do

1. Release academic/non-profit versions of Mythos/Cyber at no cost
2. Publish threat modeling explaining why restrictions exist
3. Allow independent audits of access control systems
4. Sunset restrictions on 5-year timelines (or release the models)
5. Provide free access to government security agencies

None of this would compromise safety—it would actually improve collective defense.

The Vucense Verdict

Both Anthropic and OpenAI are making the same strategic choice: Monopolize powerful security tools to cement vendor lock-in.

They’re using “responsible AI” and “safety concerns” as cover for what is fundamentally a business decision to commodify security.

Sovereignty Score Breakdown

Company	Mythos/Cyber Access Model	Transparency	Responsibility	Score
Anthropic	Enterprise-first + undefined “trusted researchers”	Low (no published criteria)	Medium (claims safety)	3/10
OpenAI	Enterprise-first	Low (closed discussion)	Low (conflicting statements)	2/10
Open-Source Alternatives	Public, community-driven	High	High (transparent trade-offs)	8/10

The Path Forward

If you’re building security tools or working in security:

1. Don’t rely on proprietary AI for vulnerability assessment. Invest in open-source alternatives (CodeQL, Semgrep, Trivy)
2. Advocate for public-interest exemptions to Mythos/Cyber restrictions
3. Support open-source security models that don’t gatekeep access
4. Document the impact of restricted access on your security posture

The irony is delicious: Companies claiming to make the world more secure are actually making it less so by hoarding the tools that defend it.

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Take Action: Demand Better Security for All

As a Developer/Startup:

1. ✅ Publish case studies of how restricted access impacts your security
2. ✅ Support open-source alternatives (fund CodeQL, Semgrep projects)
3. ✅ Push back on vendors (demand transparency, publish decisions)
4. ✅ Build open-source tools (models trained on public data)

As a Researcher:

1. ✅ Apply to Mythos access (document rejection reasons)
2. ✅ Build competing models (publish to arXiv, open-source)
3. ✅ Publish threat models (explain why restrictions don’t work)
4. ✅ Advise policymakers (EU AI Act is still developing)

As a Citizen/Advocate:

1. ✅ Support regulation (EU AI Act transparency requirements)
2. ✅ Fund open-source security (donate to projects)
3. ✅ Vote with your wallet (use privacy-first AI tools)
4. ✅ Share this article (educate your community)

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Demand Better: How to Fight Back

As a Developer/Startup:

1. Publish case studies of how restricted access impacts your security
2. Support open-source alternatives (fund CodeQL, Semgrep projects)
3. Push back on vendors (demand transparency, publish decisions)
4. Build open-source tools (models trained on public data)

As a Researcher:

1. Apply to Mythos access (document rejection reasons)
2. Build competing models (publish to arXiv, open-source)
3. Publish threat models (explain why restrictions don’t work)
4. Advise policymakers (EU AI Act is still developing)

As a Citizen/Advocate:

1. Support regulation (EU AI Act transparency requirements)
2. Fund open-source security (donate to projects)
3. Vote with your wallet (use privacy-first AI tools)
4. Share this article (educate your community)

Read More:

• Open Source AI: The Sovereignty Battle
• AI Ethics: Year of Truth
• Vulnerability Management Guide

Understanding the Models

Q: What’s the difference between Mythos and Cyber?
A:

• Mythos (Anthropic): General-purpose vulnerability discovery across all software types (Windows, Linux, mobile, IoT)
• Cyber (OpenAI): Enterprise security-focused, integrates with enterprise tools (SIEM, vulnerability management platforms)

Both use similar techniques but target different markets.

Q: Could Mythos/Cyber really be used for offensive attacks?
A: Theoretically, yes. But threat actors already have custom 0-day discovery tools and use simpler heuristics. Restricting access to legitimate researchers is overkill. For context: CodeQL (public tool) achieves 70-80% of Mythos accuracy.

Q: How accurate are these models on real-world code?
A: Mythos: 94% on known CVEs, 73% on novel 0-days. Cyber: Estimated 85-90% on enterprise software (lower than Mythos because enterprise codebases are larger and more complex).

Q: Can these models generate working exploit code?
A: Yes, both can generate proof-of-concept (PoC) code for vulnerabilities they identify. This is the core threat model for restrictions.

Restriction Models & Economics

Q: Why would Anthropic/OpenAI risk the backlash from restricting these tools?
A: Because the upside is enormous:

• Pricing: Enterprise vulnerability management tools cost $50K-$500K/year
• Mythos/Cyber pricing: Likely $100K-$1M/year for exclusive access
• Market size: 50,000+ enterprises = $5-50 billion TAM (total addressable market)

Profit incentive far exceeds reputational damage.

Q: Are there restrictions on Mythos/Cyber access, or are they truly unavailable?
A: Somewhere in between:

• Anthropic: Available to “trusted researchers” via application process (3-6 month wait, low approval rate)
• OpenAI: Only available to enterprise customers (minimum $100K/year contract)

Neither is absolutely unavailable, but practically unreachable for most developers.

Q: What’s the long-term business strategy?
A:

1. Phase 1 (Now): Restrict access, build enterprise dominance
2. Phase 2 (2-3 years): Release watered-down public versions
3. Phase 3 (5+ years): Full open-source (if competitive pressure forces it)

This is called “embrace, extend, extinguish” in security.

Open-Source Alternatives

Q: Are there open-source alternatives to Mythos/Cyber?
A: Partially:

Tool	Accuracy vs. Mythos	Cost	Access	Verdict
CodeQL	70-80%	Free (or $500K for enterprise)	Open-source	Best public option
Semgrep	65-75%	Free + enterprise tiers	Open-source	Lightweight, fast
Trivy	60-70%	Free	Open-source	Best for containers
OWASP Dependency-Check	50-60%	Free	Open-source	Very basic
Bandit (Python)	40-50%	Free	Open-source	Language-specific

Recommendation: Start with CodeQL (best accuracy). If you need 90%+ accuracy, you’ll eventually need to pay (either to Anthropic/OpenAI or build custom).

Q: Could I build my own Mythos-like tool?
A: Technically yes, but:

• Training data: You’d need labeled CVE datasets (available but limited)
• Model cost: Fine-tuning a frontier model costs $100K-$1M
• Inference cost: Running it at scale costs significant compute
• Talent: You need security ML experts (expensive)

Verdict: Possible for well-funded teams, impractical for most.

Government & Regulation

Q: Will the government intervene?
A: Varies by region:

• US: Unlikely in next 2-3 years (favors industry self-regulation)
• EU: Possible under AI Act (requires transparency, non-discrimination)
• China: Unlikely (supports domestic AI dominance)
• India: Possible via DPDP Act if data misuse occurs

Q: Could this be challenged as antitrust violation?
A: Possibly. If Mythos/Cyber become de facto standards and restrict competition, antitrust arguments emerge. But current restrictions don’t clearly violate antitrust law (restricted access ≠ monopolistic tying).

Sovereignty & Long-Term Strategy

Q: What should governments/orgs do?
A:

1. Invest in open-source alternatives: Fund CodeQL development, maintain Semgrep
2. Fund research models: Support universities building public vulnerability discovery models
3. Data sovereignty: Require local models for critical infrastructure (defense, finance, healthcare)
4. Regulation: Mandate transparency if these tools become industry-critical

Q: Should we expect both companies to eventually open-source these models?
A: Not unless forced by:

1. Regulation (AI Act transparency requirements)
2. Competitive pressure (open-source variant dominates)
3. Reputational damage (becomes impossible to sell)

Current incentives favor permanent restriction.

Q: Is this the new arms race in AI?
A: Yes. Security models are becoming like nuclear weapons:

• Developed first by superpowers (Anthropic, OpenAI)
• Restricted to allies (enterprise customers)
• Subject to export controls (OFAC, BIS)
• Eventually proliferate (in 5-10 years)

But unlike nukes, we can democratize security AI. It just requires will.

Practical Recommendations

Q: If I need vulnerability discovery, what should I use today?
A:

• Option 1 (Best): CodeQL + Semgrep combo (free + powerful)
• Option 2 (Enterprise): Buy Mythosis/Cyber (if budget allows)
• Option 3 (DIY): Fine-tune open-source model (expensive, only for well-funded teams)
• Option 4 (Hybrid): Use CodeQL + hire security researchers

Q: Should startups build security tools around Mythos/Cyber?
A: No. Too risky. Anthropic/OpenAI could:

• Increase prices (lock-in effects)
• Restrict API access (shut you down)
• Launch competing product (kill your market)

Build on CodeQL or open-source alternatives instead.

Q: How do I advocate for opening these models?
A:

1. File public comments on AI regulations (EU AI Act, US Executive Order)
2. Support open-source security projects (fund, contribute)
3. Pressure enterprises to demand transparency
4. Build competing open-source tools
5. Document impact of restricted access (case studies)

Related Vucense Resources

• Post-Quantum Cryptography Guide
• Confidential Computing: Hardware-Level Privacy
• Open Source AI: The Sovereignty Battle