Key Takeaways
- A joint report by the Cloud Security Alliance, SANS Institute, and OWASP warns that AI‑powered tools like Claude Mythos are dramatically lowering the cost and time needed for attackers to discover and exploit vulnerabilities.
- While defenders can also use AI to speed up patching and response, the inherent “bureaucracy and lag” in large organizations creates an asymmetric advantage for threat actors.
- Testing by the UK’s AI Security Institute shows Claude Mythos can solve roughly three‑quarters of expert‑level Capture the Flag problems and complete an average of 24 out of 32 steps in a simulated corporate‑network attack, narrowing the gap between novice and mid‑level hackers.
- The model struggled with an operational‑technology cooling‑tower scenario, but researchers noted the failure occurred during the IT portion, suggesting AI’s OT exploitation potential remains significant.
- Experts emphasize that the real danger lies in the vast amount of forgotten or unmaintained firmware, routers, and legacy systems—technical debt—that AI can now weaponize at scale.
- Organizations are urged to accelerate AI adoption for defense, overhaul incident‑response playbooks, and treat integration of automated tools as a core battlefield where bureaucracy and supply‑chain realities must be addressed.
Introduction and Context
The rapid evolution of large language models (LLMs) is reshaping the cybersecurity threat landscape, enabling both attackers and defenders to automate complex tasks. A newly released joint report from the Cloud Security Alliance (CSA), the SANS Institute, and the Open Worldwide Application Security Project (OWASP) brings together senior policymakers, former government officials, and industry leaders to assess how LLMs such as Anthropic’s Claude Mythos are altering the balance of power between offense and defense. The report frames the issue as an emerging “asymmetric” challenge: while AI can accelerate defensive processes, the structural delays inherent in large enterprises give attackers a relative edge.
Findings of the CSA‑SANS‑OWASP Joint Report
According to the report, the cost and capability floor for exploit discovery is falling sharply, and the window between vulnerability disclosure and weaponization is compressing toward zero. Capabilities that once required nation‑state resources are now becoming broadly accessible to lower‑skill actors. Authors Robert Lee (SANS Institute’s Chief AI Officer), Gadi Evron (CEO of Knostic), and Rich Mogull (CSA chief analyst) argue that organizations will likely be overwhelmed in the near term unless they radically accelerate their own AI‑driven defenses and update incident‑response procedures to match the speed of automated attacks.
Role of Contributing Experts and Reviewers
The report benefits from the insights of high‑profile contributors, including Jen Easterly (former CISA director), Rob Joyce (ex‑White House and NSA cybersecurity official), and Chris Inglis (former National Cyber Director). Private‑sector leaders such as Heather Adkins (Google CISO), Katie Moussouris (CEO of Luta Security), and Sounil Yu (CTO of Knostic) also contributed, along with roughly seventy CISOs, CTOs, and other security executives who served as editors and reviewers. This blend of governmental and industry expertise lends the analysis credibility and underscores the cross‑sector concern about AI‑enabled threats.
UK AI Security Institute’s Testing of Claude Mythos
Separately, the United Kingdom’s AI Security Institute (AISI) evaluated a preview version of Claude Mythos using Capture the Flag (CTF) exercises and cyber‑range simulations. AISI described the model as a “step up” from earlier Anthropic offerings, noting its ability to execute multi‑stage attacks on vulnerable networks and to discover and exploit vulnerabilities autonomously. The testing aimed to quantify how much LLMs lower the technical barrier for conducting sophisticated offensive operations.
Performance in Capture the Flag and Cyber Range Exercises
In CTF challenges, Mythos successfully solved nearly three‑quarters (73 %) of expert‑level problems—a milestone that, prior to April 2025, no LLM had achieved. In cyber‑range tests designed to mimic complex, multi‑chain attacks on a corporate network, the model was subjected to a 32‑step attack playbook ranging from initial network access to full takeover. Across ten simulations, Mythos completed an average of 24 steps, more than doubling the performance of older Claude models and other frontier LLMs, which never exceeded an average of 16 steps. These results indicate a substantial uplift in the model’s capacity to orchestrate prolonged, coordinated intrusions.
Limitations Observed in Operational Technology Tests
When tested against a simulated operational‑technology (OT) cooling‑tower environment, Mythos failed to complete the exercise. However, AISI researchers clarified that the breakdown occurred during the IT segment of the scenario, not the OT‑specific components. This nuance suggests that while the model may still lack fine‑grained proficiency in certain OT protocols, its core abilities in network reconnaissance, credential harvesting, and lateral movement remain potent enough to threaten OT‑adjacent systems.
Implications for Defenders: Asymmetric Advantage for Attackers
Both the US and UK analyses converge on the idea that LLMs are narrowing the proficiency gap between amateur “script kiddies” and more skilled hackers. By automating steps that previously required deep expertise—such as vulnerability chaining, exploit crafting, and post‑exploitation maneuvering—LLMs enable a broader pool of actors to conduct attacks that once demanded specialized teams. Defenders, meanwhile, must contend with legacy processes, approval chains, and compliance checks that slow the deployment of counter‑measures, creating an asymmetric environment where attackers can iterate and strike faster than organizations can patch.
Technical Debt and the Expanding Attack Surface
Casey Ellis, CTO and founder of Bugcrowd, warned that AI tools are “living in the places we stopped looking a decade ago,” highlighting forgotten firmware, abandoned routers, and other legacy components as fertile ground for exploitation. He likened the situation to turning a defender’s dilemma knob from ten to seven hundred, emphasizing that the sheer volume of technical debt now exposed to AI‑driven discovery vastly expands the attack surface. Organizations that have deferred maintenance or rely on end‑of‑life hardware are particularly vulnerable, as LLMs can systematically identify and weaponize these overlooked weaknesses at scale.
Challenges for Organizations in Adopting AI‑Driven Defense
The joint report urges firms and governments to accelerate the adoption of AI for defensive purposes—such as automated vulnerability scanning, prioritized patching, and real‑time threat hunting—while simultaneously revising incident‑response playbooks to accommodate machine‑speed decision‑making. However, contributors note that integration into production environments remains a battleground: lag caused by procurement cycles, security‑tool compatibility issues, and supply‑chain dependencies can blunt the benefits of AI. Overcoming these hurdles will require not only technological investment but also cultural shifts toward faster, more agile security governance.
Conclusion and Recommendations
The collective evidence points to a near‑term future in which AI‑enhanced offensive capabilities outpace the ability of many organizations to defend themselves, primarily due to institutional inertia rather than technological deficiency. To mitigate this risk, leaders should: (1) prioritize AI‑powered defensive tools that can match the speed of LLM‑driven exploit discovery; (2) streamline patch management and vulnerability‑triage workflows to reduce mean‑time‑to‑remediate; (3) invest in continuous monitoring of legacy and OT environments; and (4) foster cross‑functional teams that can bypass bureaucratic delays when responding to AI‑generated threats. By treating AI integration as a core operational imperative rather than an optional upgrade, organizations can begin to rebalance the asymmetric advantage currently favoring attackers.