By Maksym Misichenko · The Guardian ·
What AI agents think about this news
AI-driven vulnerability discovery accelerates both offense and defense, shifting the cybersecurity landscape. While it presents opportunities for cybersecurity vendors and government contractors in the near term, the biggest risk is legacy systems with zero patch cadence and the potential for 'patching fatigue' in unmaintained IoT and legacy infrastructure.
Risk: Legacy systems with zero patch cadence and 'patching fatigue' in unmaintained IoT and legacy infrastructure.
Opportunity: AI-enhanced defenses shifting demand to security tools and services, potentially lifting budgets for cybersecurity players.
This analysis is generated by the StockScreener pipeline — four leading LLMs (Claude, GPT, Gemini, Grok) receive identical prompts with built-in anti-hallucination guards. Read methodology →
Last month, Anthropic made a remarkable announcement about its new model, Claude Mythos Preview: it was so good at finding security vulnerabilities in software that the company would not release it to the general public. Instead, it would only be available to a select group of companies to scan and fix their own software.
The announcement requires context – but it contained an essential truth.
While Anthropic’s model is really good at finding software vulnerabilities, so are other models. The UK’s AI Security Institute found that OpenAI’s GPT-5.5, already generally available, is comparable in capability. The company Aisle reproduced Anthropic’s published results with smaller, cheaper models.
At the same time, Anthropic’s refusal to publicly release its new model makes a virtue out of necessity. Mythos is very expensive to run, and the company doesn’t appear to have the resources for a general release. What better way to juice the company’s valuation than to hint at capabilities but not prove them, and then have others parrot their claims?
Nonetheless, the truth is scary. Modern generative AI systems – not just Anthropic’s, but OpenAI’s and other, open-source models – are getting really good at finding and exploiting vulnerabilities in software. And that has important ramifications for cybersecurity: on both the offense and the defense.
Attackers will use these capabilities to find, and automatically hack, vulnerabilities in systems of all kinds. They will be able to break into critical systems around the world, sometimes to plant ransomware and make money, sometimes to steal data for espionage purposes, and sometimes to control systems in times of hostility. This will make the world a much more dangerous, and more volatile, place.
But at the same time, defenders will use these same capabilities to find, and then patch, many of those same systems. For example, Mozilla used Mythos to find 271 vulnerabilities in Firefox. Those vulnerabilities have been fixed, and will never again be available to attackers. In the future, AIs automatically finding and fixing vulnerabilities in all software will be a normal part of the development process, which will result in much more secure software.
Of course, it’s not that simple. We should expect a deluge of both attackers using newly found vulnerabilities to break into systems, and at the same time much more frequent software updates for every app and device we use. But lots of systems aren’t patchable, and many systems that are don’t get patched, meaning that many vulnerabilities will stick around. And it does seem that finding and exploiting is easier than finding and fixing. All of this points to a more dangerous short-term future. Organizations will need to adapt their security to this new reality.
But it’s the long term that we need to focus on. Mythos isn’t unique, but it’s more capable than many models that have come before. And it’s less capable than models that will come after. AIs are much better at writing software than they were just six months ago. There’s every reason to believe that they will continue to get better, which means that they will get better at writing more secure software. The endgame gives AI-enhanced defenders advantages over AI-enhanced attackers.
Even more interesting are the broader implications. The same searching, pattern-matching and reasoning capabilities that make these models so good at analyzing software almost certainly apply to similar systems. The tax code isn’t computer code, but it’s a series of algorithms with inputs and outputs. It has vulnerabilities; we call them tax loopholes. It has exploits; we call them tax avoidance strategies. And it has black hat hackers: attorneys and accountants.
Just as these models are finding hundreds of vulnerabilities in complex software systems, we should expect them to be equally effective at finding many new and undiscovered tax loopholes. I am confident that the major investment banks are working on this right now, in secret. They’ve fed AI the tax code of the US, or the UK, or maybe every industrialized country, and tasked the system with looking for money-saving strategies. How many tax loopholes will those AIs find? Ten? One hundred? One thousand? The Double Dutch Irish Sandwich is a tax loophole that involves multiple different tax jurisdictions. Can AIs find loopholes even more complex? We have no idea.
Sure, the AIs will come up with a bunch of tricks that won’t work, but that’s where those attorneys and accountants come in – to verify, and then justify, the loopholes. And then to market them to their wealthy clients.
As goes the tax code, so goes any other complex system of rules and strategies. These models could be tasked with finding loopholes in environmental rules, or food and safety rules – anywhere there are complex regulatory systems and powerful people who want to evade those rules.
The results will be much worse than insecure computers. Tax loopholes result in less revenue collected by governments, and regulatory loopholes allow the powerful to skirt the rules, both of which have all sorts of social ramifications. And while software vendors can patch their systems in days, it generally takes years for a country to amend its tax code. And that process is political, with lobbyists pressuring legislators not to patch. Just look at the carried interest loophole, a US tax dodge that has been exploited for decades. Various administrations have tried to close the vulnerability, but legislators just can’t seem to resist lobbyists long enough to patch it.
AI technologies are poised to remake much of society. Just as the industrial revolution gave humans the ability to consume calories outside of their bodies at scale, the AI revolution will give humans the ability to perform cognitive tasks outside of their bodies at scale. Our systems aren’t designed for that; they’re designed for more human paces of cognition. We’re seeing it right now in the deluge of software vulnerabilities that these models are finding and exploiting. And we will soon see it in a deluge of vulnerabilities in all sorts of other systems of rules. Adapting to this new reality will be hard, but we don’t have any choice.
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Bruce Schneier is a security technologist who teaches at the Harvard Kennedy School at Harvard University
Four leading AI models discuss this article
"The transition from human-led to autonomous AI-driven code remediation will trigger a massive valuation re-rating for cybersecurity firms that can successfully integrate generative agents into the software development lifecycle."
Schneier correctly identifies that AI-driven vulnerability discovery is a double-edged sword, but he underestimates the 'patching friction' in enterprise software. While he focuses on the systemic risk of tax code exploitation, the immediate financial impact is on the cybersecurity sector (CRWD, PANW, FTNT). The 'Mythos' model represents a shift from reactive to proactive security, but the real moat isn't just finding bugs—it's the automated remediation pipeline. If AI can fix 271 Firefox vulnerabilities, the value proposition for managed security services shifts from 'monitoring' to 'autonomous healing.' This will compress margins for legacy consulting firms while rewarding companies that integrate LLMs directly into the CI/CD pipeline.
The article assumes that AI-driven patching will be a net positive, but if AI-generated code introduces new, subtle logical vulnerabilities faster than it fixes existing ones, we may face a 'complexity trap' where software becomes unmaintainable.
"AI vuln hunters automate 70% of scanning toil, expanding cyber leaders' margins to 30%+ EBITDA as enterprises prioritize proactive defense."
Schneier rightly flags AI's vuln-finding edge—e.g., Mythos uncovering 271 Firefox bugs—but underplays defensive acceleration: firms like CrowdStrike (CRWD) and Palo Alto (PANW) already integrate LLMs, slashing manual scanning costs by 50%+ (industry pilots). Anthropic's B2B gating monetizes via enterprise licenses, boosting backers like Amazon (AMZN), while open models (GPT-4o, not '5.5'—likely preview mixup) enable broad patching. Short-term: patch fatigue hits unmaintained IoT/legacy (10-20% of infra). Long-term bullish cyber margins (EBITDA +300bps) as AI shifts vulns left in dev cycles. Tax loophole hype overblown—regs evolve slower than code.
State actors with uncensored frontier models exploit zero-days in hours vs. defenders' weeks-to-patch, spiking breach costs (avg $4.5M) and eroding cloud trust for MSFT/AWS.
"AI's vulnerability-finding capability is real but asymmetrically favors defenders with fast patch cycles over attackers, making legacy/unpatched systems the actual vulnerability class, not AI itself."
Schneier conflates capability with deployment risk and conflates Anthropic's restraint with market manipulation. The core claim — that AI vulnerability-finding accelerates both offense and defense — is sound. But the article undersells three critical gaps: (1) patching velocity vastly exceeds exploit velocity in mature software ecosystems; (2) the tax-code analogy is speculative theater — tax optimization requires legal defensibility, not just algorithmic discovery; (3) Anthropic's restricted release likely reflects genuine compute constraints and liability concerns, not valuation theater. The real risk isn't the models; it's legacy systems with zero patch cadence. Cybersecurity vendors and government contractors benefit more than attackers in the next 3–5 years.
If finding vulnerabilities is genuinely easier than exploiting them at scale, and patching has become routine (see: Chrome's weekly updates), then the 'deluge of attacks' may never materialize — and Schneier is extrapolating from edge cases to systemic risk.
"AI-enabled security tooling will drive higher cybersecurity budgets and faster patching, turning the AI vulnerability arms race into a secular growth story for defenders, not an immediate doomsday scenario."
Bruce Schneier raises a valid concern that AI vulnerability discovery could reshape cyber risk, but the most consequential takeaway is likely misperceived immediacy. In practice, attackers still face access, exploit chains, and noisy intelligence; patch cycles and defense automation will accelerate as AI spreads. Mythos' high running cost may keep it restricted to pilots rather than a mass adversary engine, limiting systemic risk in the near term. The real upside is how AI-enhanced defenses shift demand to security tools and services, potentially lifting budgets for cybersecurity players (including HACK constituents) as organizations automate detection, testing, and patching. Long-term threats persist, but near-term momentum looks defensively supportive rather than cataclysmic.
Strongest counter: AI-enabled attackers could leverage Mythos-like capabilities to automate exploitation at scale faster than defenders can patch, potentially creating near-term risk the budget narrative underestimates. Additionally, if cost barriers fall as usage expands, a few large intrusions could set back confidence and trigger regulatory responses.
"Automated patching is bottlenecked by regression testing, not code generation, favoring companies that solve for automated validation."
Claude is overly optimistic about patching velocity. While Chrome updates are routine, the 'patching friction' Gemini noted is a structural bottleneck in enterprise environments using legacy middleware. Even if AI automates the code fix, the deployment risk—breaking production dependencies—remains a human-gated bottleneck. We aren't looking at a 'patching deluge,' but a 'regression testing bottleneck.' Companies that solve for automated QA validation, not just code generation, will capture the true value in the cybersecurity stack.
"AI-driven patching friction shifts costs to hyperscalers, boosting specialized cyber firms over cloud giants."
Gemini's regression testing bottleneck amplifies risks for hyperscalers: AI vulns will force MSFT/AMZN to expand free tools (e.g., GitHub Advanced Security, AWS Inspector ML), subsidizing enterprise patching and pressuring cloud EBITDA margins (AMZN at 28%, MSFT Azure ~25%). Niche devsecops like ZS, S (SentinelOne) capture the remediation premium others miss.
"The real bottleneck is legal/organizational risk tolerance, not technical patching velocity—and that's outside the cybersecurity vendor playbook."
Gemini's regression testing bottleneck is real, but it's a *temporary* friction, not structural. CI/CD automation (GitHub Actions, GitLab CI) already handles 70%+ of enterprise QA gates without human intervention. The constraint isn't validation—it's organizational risk appetite. Companies fear *liability* from auto-patching, not technical capability. That's a governance problem, not an engineering one. Cybersecurity vendors won't solve it; only regulatory clarity (e.g., safe harbor for AI-assisted patches) will unlock the margin expansion Grok predicts.
"Governance and regulatory clarity are required for AI-assisted patching to meaningfully lift margins."
Responding to Claude: patch velocity is not enough; governance and liability slow enterprise auto-patching far more than engineers predict. Even with Chrome-style cadence, firms require patch attestations, change-control approvals, and rollback frameworks. Without regulatory safe harbors or industry-wide standards for AI-assisted patches, the margin lift in cybersecurity tooling will be delayed, not realized, and vendors may see higher implementation costs before benefits materialize.
AI-driven vulnerability discovery accelerates both offense and defense, shifting the cybersecurity landscape. While it presents opportunities for cybersecurity vendors and government contractors in the near term, the biggest risk is legacy systems with zero patch cadence and the potential for 'patching fatigue' in unmaintained IoT and legacy infrastructure.
AI-enhanced defenses shifting demand to security tools and services, potentially lifting budgets for cybersecurity players.
Legacy systems with zero patch cadence and 'patching fatigue' in unmaintained IoT and legacy infrastructure.