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.
