This week, the technical team of the U.S. Treasury Department was exposed to be seeking access to Anthropic's high-risk model Mythos, intending to use it for security and vulnerability detection in key systems of the Treasury. This move came after intense consultations between the Federal Reserve and Wall Street regarding systemic risks related to AI, and the timing itself carries strong policy and security signals. Unlike ordinary large models, access to Mythos is currently strictly controlled by a few institutions, marked as a highly sensitive resource. The Treasury's choice to proactively engage under such constraints amounts to wagering on a nascent "AI against AI" approach: using the strongest generative AI for defense while also objectively approaching the other side where it is weaponized and used for cyberattacks, giving the entire story a distinctly gambling color.

The Treasury Eyes High-Risk Models: Time Pressure for AI Defense

According to public reports, the technical team of the U.S. Treasury Department has initiated contact with Anthropic, with a very clear goal: to obtain access to Mythos and quickly integrate it into the Treasury's internal security testing processes. The internal goal set by the Treasury's Chief Information Officer Sam Kokos is to gain access "within this week," a timeframe measured in weeks reflecting the team’s dissatisfaction with existing cybersecurity capabilities and urgency regarding potential threats. The compression of time itself signals a preference for risk.

In the Treasury's vision, Mythos will be used as an "enhanced red team," conducting high-intensity penetration simulations and vulnerability explorations on key systems within a controlled environment. Compared to traditional security tools, it is expected to uncover deeper and more hidden system weaknesses, and help the Treasury establish a future-oriented AI-native security assessment framework. Within the entire landscape of U.S. financial security, this deployment resembles an experimental front line: not merely adding a firewall to old systems but attempting to reshape defense logic itself using high-risk AI.

Restricted Mythos: A Sensitive Tool in the Hands of a Few Institutions

The reason the Treasury needs to "queue up" for access to Mythos is because Anthropic has explicitly categorized it as a high-risk model, providing access only to a very limited number of institutions. Related statements emphasize that "Access is still strictly limited to a few institutions," sending a signal to the outside world: this is not a general product that can be easily commercialized or scaled; rather, it is a sensitive technological asset that requires case-by-case scrutiny and strict licensing.

The official reasoning from Anthropic focuses on the potential misuse risks associated with the model. On one hand, they acknowledge the cutting-edge value of high-capability models in security testing and cyber defense scenarios; on the other hand, they continuously emphasize the dangers of Mythos being used for cyberattacks, reminding all parties to consider its attack assistance capabilities cautiously. This release posture, inherently carrying a "risk label," makes Mythos more like a “high-risk tool” that must be handled with care, rather than a typical enterprise-level SaaS service.

Under such premises, what the Treasury is striving to obtain is not a simple API Key, but a strictly scrutinized access qualification. This not only indicates that the Treasury is viewed as a high-trust entity with sufficient governance capability, but also highlights: any institution gaining access to Mythos must confront the persistent ambiguous boundary between their operations and cyberattack capabilities.

Defense or Weaponization: The Symmetrical Dilemma of AI's Double-Edged Risks

Internal documents of the Treasury mention the “ultimate threat posed by powerful AI systems,” pointing out the true contradiction in the current game: the stronger the model, the more likely it can be amplified on both defense and attack ends. Using Mythos to probe the Treasury's own system vulnerabilities logically aims to expose risks in advance and strengthen defenses; but in terms of symmetry, models of equal or even more aggressive capabilities could also be used by hackers or hostile entities to optimize attack paths.

This symmetrical risk makes "AI against AI" sound like a naturally evolving security path: if attacking parties may use strong models, then defenders must respond with models of equal or even greater capabilities. However, discussions about this defensive framework remain at conceptual and experimental levels, more akin to a security paradigm waiting for validation than an established fact. The Treasury's bet on Mythos essentially enters into this still-forming race, hoping to position itself at the forefront of the technology curve while also being forced to accept higher uncertainty.

On a practical level, the symmetrical dilemma is reflected in a simple question: can the Treasury ensure that the security knowledge and vulnerability mappings generated from using Mythos will not, in turn, become "textbooks" for others' attacks? Without mature governance models and comprehensive constraint mechanisms in place, each invocation of high-risk AI inevitably carries this structural risk of "defense equating to exposure."

Regulatory Nerves on Edge: From Federal Reserve Discussions to Treasury Experiments

The Treasury's connection with Mythos is not an isolated incident but an extension of the overall nervous tension within the U.S. financial regulatory system. Research briefs indicate that the Federal Reserve has recently engaged with several Wall Street institutions regarding systemic risks posed by AI, discussing key points including: the chain reaction of model failures in high-frequency decision-making, market resonance triggered by algorithmic consistency, and the vulnerabilities arising from the rapid increase in dependencies on AI in financial infrastructures.

In this macro context, the Treasury's move to seek access to Mythos can be viewed as a new phase in the regulatory system's trend of "embracing high-risk AI": no longer merely managing market participants from compliance, reporting, and external constraint angles, but actively engaging in the fray by incorporating cutting-edge models into its own security stack, attempting to build hedging capabilities at a technological level. Regulators are shifting, in part, from rule-makers to users of high-risk AI, subtly altering their role.

This shift introduces a new game:

● On one hand, financial regulatory bodies hope to leverage cutting-edge models to enhance threat perception and defense speed, avoiding being caught off guard when "AI-driven attacks" truly mature;

● On the other hand, they are acutely aware that introducing high-capability models on a large scale into critical infrastructure may inherently amplify systemic risks— including model misjudgments, vendor lock-in, and the accountability challenges in the event of technological failures.

The Treasury's engagement with Mythos stands at the midpoint of this tension curve: reflecting the regulator's desire for leading technologies while exposing their ongoing unease about their ability to manage such technologies.

The Technical Team's Bet: Handing Vulnerability Detection to High-Risk Black Boxes

From the technical team's perspective, the motivation to bet on Mythos is not complicated: traditional security tools often can only identify "overt vulnerabilities" in the face of increasingly complex systems, while truly damaging vulnerabilities often lie hidden within vast codes and intricate dependencies. They hope to utilize advanced models to discover deep security gaps quickly from a perspective closer to that of an attacker, thereby completing defensive exercises before an attack occurs.

However, alongside this technical impulse exists an ever-present skepticism from external observers. Research briefs clearly state that no public information currently exists regarding the technical assessment standards and specific effectiveness data of the Treasury's internal processes. This means it is challenging for outsiders to determine whether Mythos has indeed significantly improved security levels, or merely constructed a new layer of "technical illusion." In the absence of verifiable metrics and transparent evaluation mechanisms, this experiment appears, to observers, more like a high-risk trial betting on critical national systems.

Deeper issues arise if the Treasury forms a dependence on such high-risk, closed-source models in practice, complicating subsequent governance, auditing, and accountability for failures:

● When security decisions are largely based on model outputs, how should accountability be divided between the Treasury and the model provider for failures?

● In circumstances where the model’s capabilities and behavior lack auditable transparency, how can regulatory bodies explain to the public and legislative bodies "why they trust this black box"?

● If future political or geopolitical situations change, could excessive reliance on a specific vendor for critical security capabilities backfire as a new strategic risk?

The bet made by the Treasury's technical team concerns both the efficiency of vulnerability detection and a gamble on whether governance structures can keep pace with technological advancements.

How the Future Battlefield of AI Against AI Will Evolve

Overall, the Treasury's efforts to access Mythos condense the tension of high-risk AI in the field of financial security within the same framework: on one end lies the fear of unknown threats, driving regulators to actively embrace the most advanced defensive tools; on the other end, vigilance against the tools potentially evolving into sources of threats imbues the entire endeavor with a strong sense of caution and self-defense. The gap between defense and weaponization is the core area of this game.

From a trend perspective, "strongly likely" that "AI against AI" will gradually spread from experiments in a handful of key institutions like the Treasury and central banks to broader national security systems: financial infrastructures, energy networks, defense, and intelligence systems will all face similar choices—whether to continue relying on traditional security systems or accept high-capability models as the central line of their new defense. In this process, the competition for narrative power among countries over access to cutting-edge models, computational resources, and security standards will also evolve into a global regulatory race: whoever establishes a viable governance framework for high-risk AI first may seize the initiative in the next phase of digital security landscape.

But regardless of how technology evolves, one bottom line is becoming increasingly clear: future management of such models must establish more explicit security red lines regarding access, use boundaries, and auditability. Questions about who can use them, for what purposes, and whether decision chains can be reconstructed and accountability pursued in the event of errors must have institutional answers; otherwise, "AI against AI" will remain in the realm of high-risk experimentation and difficult to become a sustainable security infrastructure. The story between the Treasury and Mythos is merely the start of this long-term narrative.

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