Wade Woolwine is Senior Director, Product Security at Rapid7.

Rapid7 is excited to join Anthropic’s Project Glasswing, which includes access to Claude Mythos Preview, giving our teams the opportunity to explore how frontier AI can support legitimate, internal defensive security workflows led by experienced security practitioners. Anthropic has now expanded Project Glasswing from its initial cohort to a broader group of organizations, underscoring how quickly this conversation is moving from model capability to industry readiness. 

This access comes at a critical moment for security operations. Attackers are moving faster, attack surfaces are expanding, and fragmented security data makes it harder for teams to correlate context and respond at scale. The industry is entering a period where powerful frontier AI models with advanced cyber capabilities require new operating norms, stronger safeguards, and better infrastructure for how vulnerabilities are verified, disclosed, fixed, and deployed.

Frontier AI will raise expectations for how quickly security teams can understand risk, make decisions, and prove that action has reduced exposure. Rapid7 has already been tracking what Project Glasswing means for security leaders: faster discovery is only part of the story, and the real test is how defenders handle everything that follows, from prioritization and remediation to validation, detection, and response. Rapid7’s involvement gives us another opportunity to help shape how advanced LLMs are evaluated and applied to real defensive security work.

The organizations best positioned to benefit from frontier AI will be those that pair advanced models with trusted security context, expert oversight, and mature operational workflows. That is the lens Rapid7 is bringing to our internal exploration of Claude Mythos Preview, and it reflects the same principle that guides our broader AI strategy: advanced technology delivers the most value when grounded in security expertise, operational context, and measurable outcomes.

Exploring Claude Mythos Preview inside Rapid7

In the first week of Rapid7’s access to Claude Mythos Preview , it has already given our researchers, security engineers, and analysts another way to explore how frontier AI can strengthen the security workflows we already rely on. Our use is internal and practitioner-led, with a focus on learning where these models can create defensive value, where human expertise remains essential, and where responsible guardrails are required.

Cybersecurity impact depends on more than model capability. A model may help identify a potential vulnerability and confirm exploitability, but reducing risk requires deeper operational work: understanding affected systems, mapping business context, prioritizing remediation, validating the fix, and ensuring detection coverage is in place. Anthropic’s latest Project Glasswing update reinforces that same shift: as AI makes discovery faster, the next challenge becomes helping the industry scale verification, disclosure, fixing, and deployment.

For more than 25 years, Rapid7 has helped organizations understand risk in real environments and take action against it. Access to Project Glasswing gives us another way to explore how LLMs can support that mission, while reinforcing the same principle that guides our broader AI strategy: advanced technology delivers the most value when grounded in security expertise, operational context, and measurable outcomes.

How Rapid7 is using Claude Mythos Preview internally

Our initial exploration is focused on internal defensive use cases that can help strengthen our product security, improve our research, and create better security outcomes overall. The goal is to understand how frontier AI can support highly specialized security work while helping us evaluate these capabilities with the discipline and caution they require.

In product security, we are exploring how Claude Mythos Preview can support assessment of our code and infrastructure, helping identify potential vulnerabilities, weaknesses, or risky patterns that traditional product security tools may miss. Used responsibly, this type of workflow can help engineering and product security teams reduce risk earlier in the development lifecycle.

We are also evaluating how frontier AI can support vulnerability validation and exploitation analysis in authorized environments. This includes exploring how models can help researchers reason across unfamiliar code, validate severity, build safe proof-of-concept exploit paths, and translate findings into practical remediation guidance.

Our work also includes zero-day research and frontier model evaluation. As models become more capable, security teams need a clear view of where they perform well, where they struggle, and how their outputs should be governed. Evaluating these models against vulnerability discovery and exploitation tasks helps Rapid7 understand their practical value, limitations, and safeguards.

We are also applying frontier AI to red-teaming, detection, and response research. As AI becomes more embedded in enterprise systems and security operations, it also needs to be tested adversarially. Frontier models can help practitioners explore attack paths, challenge assumptions, enrich investigations, reduce noise, and support faster decisions when paired with the right telemetry and human judgment.

Why frontier AI needs cybersecurity expertise

The industry conversation around frontier AI often starts with what models can find, especially as they become more capable at reasoning across large codebases and surfacing potential flaws. However, security teams reduce risk by knowing which findings matter, acting on them quickly, and proving that exposure has been reduced. As we’ve written before, the challenge is turning faster discovery into faster action, which requires teams to understand their environment well enough to apply emerging models with intent.

That is why expertise matters. AI can help accelerate parts of the workflow, but security impact comes from connecting discovery to validation, remediation, detection, and response. Without that connection, faster discovery can create more volume for teams that are already stretched. With the right context and operating model, it can help defenders move earlier and with more confidence.

This is the lens Rapid7 brings to Project Glasswing. Our teams are exploring these capabilities as practitioners who understand the real-world pressures customers face: incomplete asset visibility, fragmented ownership, growing vulnerability backlogs, expanding identity and cloud risk, and alert volumes that can outpace human-only workflows.

From frontier AI adoption to preemptive security

Rapid7’s broader strategy is focused on helping organizations move toward preemptive security, where exposure management, and detection and response work together to disrupt attackers before risk becomes impact. As AI accelerates both attacker activity and defender workflows, security teams need more than faster vulnerability discovery. They need rich contextual prioritization, trusted AI-driven decision making, and mitigations beyond patching so they can prioritize, validate, and respond at speed and scale.

The next phase of cybersecurity will require speed, scale, and consistency across the entire security lifecycle. The industry challenge is expanding from finding vulnerabilities to the harder operational work of verifying, disclosing, fixing, and deploying remediations. While vulnerability and alert volumes will increase, cyber resilience depends on what happens both before and after discovery. In a reality where vulnerabilities can be exploited or chained together quickly, teams need the ability to prioritize exposures that have real impact, investigate quickly with full context, and keep operating in the face of disruption.

Preemptive security also means mitigation must extend beyond patching. Timely patching at scale is not always practical, so security teams need the ability to intercept and disrupt exploit paths through virtual patching, controls management, and rapid response actions. That is why Rapid7 is approaching frontier AI through the lens of preemptive security. Our AI foundation is built around unified security data and shared operational context across exposures, assets, identities, behavior, and activity, and transparent AI decisions validated by experts and governed by policy-driven workflows.

Access to Claude Mythos Preview is another step in exploring how LLMs can help security teams move earlier, act faster, and build more resilient programs without losing the human expertise and accountability that effective security requires. Anthropic also unveiled Fable 5 today, its first publicly available Mythos-class model, which will only further underscore the importance of having an integrated, AI-ready security plan that can turn this new benchmark of visibility into meaningful security improvement.

Anthropic’s Project Glasswing has sparked plenty of discussion about what AI might soon do for vulnerability discovery, but the more useful question for most security teams is how to prepare for, and more importantly seize the opportunity of, what comes next.

 As we wrote in our earlier blog, What Project Glasswing Means for Security Leaders, AI is becoming more capable of finding software flaws. The pressure that follows lands on the teams responsible for deciding what matters, validating risk, assigning ownership, and getting remediation moving across environments that were already hard to manage. We believe that the organizations that will benefit most from the next wave of AI will be the ones that understand their environment well enough to use these emerging AI models with intent, rather than layering them onto immature processes and hoping that speed alone will solve the backlog.

What this moment means for security teams

The number of publicly tracked software vulnerabilities has broken records almost every year over the last decade, while supply chain risk has continued to rise. Most teams were already feeling the strain of more findings than they could process cleanly. The Common Vulnerabilities and Exposures (CVE) program, the standard system for identifying and tracking known vulnerabilities, recorded 48,185 disclosures in 2025, a 20% increase over 2024, with roughly 40% of those disclosed vulnerabilities rated high or critical. 

The pace in 2026 was already working out to hundreds of new CVEs per day when those figures were cited. That tells you something important about the current environment: the challenge has not necessarily been  a lack of findings, but instead converting a growing stream of findings into measurable risk reduction.

The reality is that very few organizations are going to hand a model free rein over their most sensitive environments the minute those capabilities become more widely available. Trust will be built in stages: early adoption is much more likely to focus on backlog reduction, triage support, patch testing, and repetitive lower-tier remediation work that consumes time without carrying the same level of operational risk as the most critical systems in the business. That is a more realistic starting point, and it leads to a more useful question. Before teams apply AI more broadly, they need to understand their environment well enough to use it intentionally.

Establish the foundation before layering in AI

The promise from Project Glasswing and almost every other AI-powered security initiative is quite similar: leverage AI to identify patterns, summarize risk, suggest fixes, and speed up repetitive work. Regardless of technology, success  still depends on how well an organization understands its environment, the context around each finding, and the process used to act on it. 

A model can generate more output than a team ever could on its own, but that output becomes noise if the organization cannot answer basic questions about scope, ownership, criticality, and exposure. Teams need a clear, continuously updated picture of the environment before they can decide where AI should be applied, what should remain human-led, and which parts of the backlog are safe to push through more automated workflows.

The AI landscape is already shifting fast, and it will keep shifting, which is why this moment should prompt a more preemptive and resilient strategy rather than another round of tooling hype. Chasing each new capability as it arrives will inevitably force teams to keep reorganizing around the latest announcement. A stronger path is to get the foundation right first - understand the environment, the attack paths, and the assets that matter most; but most importantly, establishing the process and the people behind making these decisions. Then use AI where it meaningfully improves speed, consistency, and focus.

Why Attack Surface Management should be part of that foundation

A strong foundation starts with visibility. Security teams need a live picture of what exists in the environment, what is exposed, how assets connect to one another, and which systems carry the greatest business impact if something goes wrong. That is where Attack Surface Management becomes central. Rapid7’s approach through Surface Command is built around a continuous view of the attack surface across the digital estate, which helps teams understand where exposures sit and how they relate to internet-facing, business-critical, or otherwise high-impact systems.

That matters for AI adoption just as much as it matters for day-to-day security operations. Teams cannot apply AI strategically if they are guessing about which parts of the environment are lower priority, which assets belong to which owners, or where a newly disclosed flaw could create real business risk. A better view of the attack surface gives organizations the context they need to segment the problem properly. That makes it far easier to start with the right use cases, whether that is backlog reduction in lower-impact systems, targeted prioritization of exposed assets, or faster triage where the risk picture is already well understood.

Ownership is part of that foundation too. Remediation slows down when no one can quickly identify who owns the affected application, environment, or workflow. Security teams already lose time there today, and AI will only make that bottleneck more visible if it starts surfacing issues faster than organizations can assign them. Attack Surface Management helps turn that ambiguity into something more actionable by tying exposure to environment context and likely ownership.

How Vulnerability and Exposure Management turns visibility into action

Once the environment is understood, teams still need a way to move from findings to outcomes. That is where Vulnerability and Exposure Management becomes the operating layer that keeps the work grounded.

The biggest value here is not simply collecting more vulnerability data. It is targeted prioritization and validation. When a disclosure lands, teams need to know whether the issue affects an exposed asset, whether there is evidence of exploitation or attacker interest, whether the impacted system is business-critical, and whether existing controls already reduce some of the risk. That is the kind of context that helps organizations decide what deserves immediate attention and what can be handled through a normal remediation cycle.

This is where artificial intelligence can help move remediation forward faster. Instead of asking teams to manually connect exploit signals, asset criticality, and vulnerability intelligence on their own, AI can distill that context directly in the remediation workflow. That makes it easier to understand why an issue matters, what the likely impact is, and what to do next, which shortens the gap between discovery and a confident decision on how to respond.

We expect most organizations to use AI to assist with, or in some cases take over, lower-tier triage, backlog cleanup, summary generation, and patch support in areas where the workflow is already established and the blast radius is more manageable. Human experts still stay closest to the most critical business logic, the most sensitive environments, and the most complex remediation paths. That is a practical adoption model, and it only works when the organization already has enough structure in place to know where those boundaries are.

Curated vulnerability intelligence changes the quality of decisions

That kind of deliberate adoption only works when teams can make better decisions, faster. Security teams need more than severity scores and a long list of CVEs. They need enough context to understand what matters, what can wait, and where action will reduce real risk fastest. As Rapid7 outlined in The Power of Curated Vulnerability Intelligence, the goal is to identify the vulnerabilities that actually matter and give teams enough context to act with confidence.

That intelligence provides a form of validation that most teams need badly as disclosure volume rises. It helps answer whether a finding is tied to active attacker interest, whether proof-of-concept activity is public, whether the asset is exposed, and whether delaying a patch creates unacceptable risk. It also supports the decisions that happen in the gap between discovery and full remediation. When a patch is delayed because of change controls, testing constraints, or lack of a vendor fix, teams still need to reduce exposure. Curated intelligence helps them decide whether to use segmentation, access restrictions, configuration changes, added monitoring, or virtual patching while the longer-term fix is being worked through.

That is one of the clearest ways Rapid7 helps customers move from data to outcomes. Intelligence is fused into the workflow so teams can prioritize with more precision and validate their actions against real threat context, not just generalized scores.

How runtime and remediation fit into the broader AI story

There is another part of this story that matters as organizations think more seriously about AI-driven security operations. As AI shapes the way teams handle exposures earlier in the lifecycle, context of application at runtime matters more too.

To make that foundation complete, organizations need to look beyond static posture and bring runtime validation into the picture. When teams can identify which vulnerabilities and misconfigurations are actively exploitable in production, and map sensitive data and identity access to real-world attack paths, they get a much clearer view of actual risk. Security teams need to understand what is vulnerable, how systems behave when live, and where unusual activity may suggest a problem is moving toward exploitation. With that runtime context in place, teams can spend less time chasing theoretical vulnerabilities and more time focusing on the exposures that are actively creating risk in live environments. 

That connection between exposure, intelligence, remediation, and runtime behavior is where AI starts to become genuinely useful rather than simply impressive. It supports a more intentional model of security decision-making, one that narrows the gap between what is found, what matters, and what happens next.

What security leaders should do now

This is a good time for security leaders to step back and ask a more disciplined set of questions.

• Do we understand our environment well enough to direct AI toward the right problems? 

• Can we clearly separate higher-risk, higher-impact assets from the parts of the backlog that are mostly operational drag? 

• Is threat intelligence embedded in how we interpret findings, or are we still depending too heavily on raw severity? 

• Can we identify ownership fast enough for AI-assisted triage to result in meaningful action? 

• Are compensating controls part of the plan when remediation cannot happen immediately?

Those questions shape the quality of everything that follows.

Glasswing creates a real opportunity for security teams that are ready to use AI with more intention. AI can move work forward faster, reduce manual drag, and absorb classes of issues that currently consume time without improving outcomes. The teams that benefit most will not be the ones that rush to apply new models everywhere. They will be the ones that understand their environment, have a clear view of their attack surface, have mature enough workflows to apply AI where it makes sense, and can measure whether the actions taken actually reduced exposure.

Rapid7’s approach to building resilience is grounded in those same needs. Attack Surface Management provides the environmental foundation, Vulnerability Management drives prioritization and action, curated vulnerability intelligence strengthens validation and decision-making, AI-generated remediation insights compress the time from discovery to the next step, and runtime security adds context where live behavior matters. Together, those pieces help customers build a security program that is ready for AI rather than constantly reacting to it.

Anthropic’s Project Glasswing matters because it offers an early look at how quickly software flaws may soon be found, validated, and potentially turned into viable attack paths, even if that capability is currently limited to a closed partner program. Anthropic says its restricted Claude Mythos Preview model has already identified thousands of high-severity vulnerabilities, including flaws in major operating systems and browsers, and in some cases developed related exploits autonomously.

Some early coverage has emphasized the risks and need for restraint in deploying capabilities like this, and for most organizations, it won’t immediately change day-to-day security operations. What it does offer is a signal of where the industry may be heading: a future where discovery moves faster, and where the pressure shifts to everything that follows, including prioritization, remediation, validation, and response. Glasswing feels less like the storm itself and more like the first sign that the radar is getting better faster than the emergency plan. How well can we handle what comes next?

What is Project Glasswing?

Project Glasswing is Anthropic’s new defensive security initiative built around Claude Mythos Preview, a model the company is not releasing publicly because of its cyber capabilities. Anthropic says the preview is being provided to a limited set of organizations, including AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, Nvidia, and Palo Alto Networks, with access also extended to more than 40 additional organizations. Anthropic has also committed up to $100 million in usage credits and additional support for open-source security work. 

That makes this more than another AI feature release. Anthropic is effectively signaling two things at once. First, there is a meaningful backlog of serious, undisclosed vulnerabilities still out there. Second, capabilities like this are sensitive enough that broad public release would be irresponsible right now. For security leaders, the message is not that AI replaces human researchers. It is that AI is becoming materially more useful in vulnerability research, and defenders should be thinking now about how they will handle what comes next.

Why this matters to vulnerability management

It would be easy to read this as a story about faster vulnerability discovery alone. That misses the more important point. If Anthropic’s claims are directionally right, the immediate pressure does not land on discovery alone. It lands on everything downstream of discovery: asset context, exploitability analysis, ownership, compensating controls, patching, exception handling, validation, and detection coverage. In other words, the harder part of security becomes more obvious.

That matters because most enterprise programs do not struggle to generate findings. They struggle to decide which findings matter first, who should act, what can wait, and whether remediation actually reduced exposure. If AI pushes vulnerability discovery into a new gear, weak operating models will feel that pressure first. Backlogs get bigger. Teams drown in queues. Fix rates do not keep pace. Risk stays put. That is not a model problem. It is an execution problem. 

This is why security leaders should be careful with the framing. The headline is not “AI found bugs, therefore security improves.” The headline is that the bottleneck may be moving downstream even faster than expected. That raises the value of programs that connect exposure management, remediation, and runtime defense instead of treating them as separate activities. 

What Anthropic’s examples really tell us

Some of the reported examples are striking. Anthropic and media reports say Mythos Preview found a 27-year-old OpenBSD vulnerability, a 16-year-old FFmpeg flaw that reportedly evaded millions of automated test executions, and multiple Linux kernel vulnerabilities that could be chained together. Anthropic has also said the model reproduced vulnerabilities and built proof-of-concept exploits at a high success rate in testing. Even if individual examples get debated over time, the pattern is the important part. The model appears to compress several human steps into one workflow, from discovery to validation to exploit construction. 

Security has seen faster discovery before. Fuzzing changed the game. Better automation changed the game. Large-scale bug bounty operations changed the game. What is different here is the combination of reasoning, coding, persistence, and iteration inside a single model loop. If that loop becomes reliable, then defender workflows built for human-speed intake and triage will come under more strain. That does not make coordinated disclosure obsolete. It makes today’s processes look slow.

What CISOs should ask right now

CISOs do not need to decide this week whether Anthropic’s model changes the entire market. They do need to ask a more practical question: if my environment starts surfacing materially more vulnerabilities tomorrow, what happens next?

For many organizations, that answer is uncomfortable. Findings land in multiple tools. Asset inventory is incomplete. Internet exposure is only partly understood. Ownership is fragmented. Patch cycles are slow. Exceptions pile up. Security teams cannot easily prove that a fix changed reachable risk in the real environment.

That is where this news becomes relevant. AI-driven discovery does not reduce the need for an exposure-led security model. It increases it. The organizations that benefit most will not be the ones with the biggest pile of findings. They will be the ones that can connect those findings to business-critical assets, internet exposure, identity paths, existing detections, remediation workflows, and validation. 

A good board-level translation is that faster discovery only has value if the organization can prioritize effectively, remediate quickly, and prove that the fix reduced real exposure. Otherwise, the result is more volume and more noise.

What engineers should take away from Project Glasswing

For engineers, this announcement is less a reason to either celebrate or dismiss the technology than it is a sign that defensive research workflows may change quickly if capabilities like this spread more broadly. Today, Glasswing is still limited to a small group of trusted partners, so this is not yet a shift most engineering teams will feel directly in their daily work. What it does offer is an early look at where software security may be heading.

AI-assisted discovery is likely to become more common across secure development, code review, infrastructure testing, and open-source maintenance. That creates real opportunities. Models can help explore deep code paths faster, challenge assumptions earlier, improve reproduction, and generate more detailed reports than many conventional workflows produce today.

The harder question is what comes next. If AI can generate more findings and more exploit hypotheses, engineering teams will need stronger intake, validation, and prioritization discipline, not less. Triage quality, deduplication, severity context, reproducibility, and ownership all become more important as discovery speeds up. Many maintainers and internal product security teams already struggle with volume, and machine-generated reporting could make that problem worse if workflows do not mature alongside the tooling.

At the same time, that is only one side of the equation. If models can help find bugs faster, they may also help defenders confirm impact, suggest code changes, support patch development, and reduce some of the manual effort that slows remediation today. In the longer run, the same AI shift that increases pressure on defenders may also help them absorb some of that pressure. The real issue is not whether AI adds more findings. It is whether teams can use it to shorten the full path from discovery to decision to verified fix.

The best engineering response, then, is not to argue about whether these models are impressive. It is to improve the operating path around them. Can the team confirm impact quickly, tie a flaw to reachable attack surface, deploy a patch or control change, and verify that exposure is actually reduced in production? If that chain does not improve, faster discovery alone will not deliver much value.

What this means for the next phase of security

Anthropic’s decision to restrict access is understandable, but it also underscores a harder truth - capabilities like this rarely stay contained for long. Whether through competitors, open customization, or less restrained releases, the broader industry should assume similar models will become more widely available in the near term. For most organizations, this is not a market-wide operational shift today. It is a warning of what may be closer than it appears.

That signal arrives at a time when many security operations teams are already under strain. Most can investigate only a fraction of the alerts and exposures their environments generate, which keeps them in reactive mode, manually triaging high-priority signals across fragmented telemetry while scale and consistency remain difficult to achieve. Many promises of AI super-productivity have not yet translated into day-to-day operational relief. That is part of what makes Glasswing worth paying attention to. It points to a future where discovery may improve faster than most response models do.

It also points to an opportunity. If AI can compress parts of vulnerability research, the same broader class of capabilities may eventually help defenders improve prioritization, investigation, remediation, and validation as well. That is where the next phase of security is likely to be decided. Not in whether organizations can generate more findings, but in whether they can use AI to make response workflows faster, more consistent, and more precise.

From our perspective, that raises the operational bar for defenders. If discovery gets faster, organizations will need to shorten time to detect, accelerate time to patch, and manage vulnerability backlogs with far more urgency than they do today. That starts with a threat-led view of the environment. Teams need to understand which weaknesses are most exposed, most exploitable, and most likely to matter in real attack paths so they can prioritize action based on actual risk, not just queue depth.

That is the practical lesson from Glasswing. It feels less like the storm itself and more like the first sign that the radar is getting better faster than the emergency plan. For most organizations, the announcement does not change the queue tomorrow morning. What it does change is the urgency of preparing for a future in which discovery, triage, and response may all begin moving at a very different pace.