How the Campus Cyber assesses Mythos’ impact? In early April 2026, Anthropic rolled out its new frontier AI model, turning cybersecurity into the testing ground for its announcement.
The effect is immediate: IT and cyber teams worldwide find themselves under pressure to explain what changes this brings, without always having the information needed to answer.
Initial assessments, notably from the UK AISI (the British AI Security Evaluation Authority), confirm a significant improvement in the model’s cybersecurity capabilities: vulnerability detection, exploitation, chaining information to construct attack paths, and moving from point testing to large-scale automated discovery.
American cybersecurity firms that are members of the Glasswing alliance already claim that Mythos allows them to compress a year of human pentesting into three weeks. Security patches directly attributed to AI-assisted discoveries have even been published, including for widely used software such as Firefox.
An important nuance: Europe does not yet have an independent evaluation of the model. The only sources available are American and British.
The Campus Cyber urges maintaining a level-headed stance: the figures claimed about training parameters or performance should be treated with care, as long as information asymmetry with Anthropic persists.
Mythos is not a rupture, it’s an acceleration
Perhaps the most important point of the note: Mythos is not an anomaly.
It is one more point on the exponential curve of AI applied to cybersecurity. Previous models already detected vulnerabilities. What changes with Mythos is the combination of capabilities (detection + exploitation + reasoning + prioritization + scaling) and the acceleration of the timeline.
OpenAI rolled out GPT 5.4 Cyber two weeks after Mythos Preview, followed by version 5.5 in early May. The momentum won’t reverse. According to Campus Cyber experts, equivalent open-source models, potentially of Chinese origin, could be available to the public by the end of 2026.
This means AI is no longer a theoretical threat in cybersecurity. It becomes a structural operational parameter. The question is no longer “if” but “when” and “at what speed.”
The scenario that concerns experts most is the massive discovery of zero-day vulnerabilities by AI. In other words, a purge-like effect, in a single block, of long-standing flaws housed in widely deployed software such as legacy banking environments, industrial systems, and critical infrastructure.
This peak is considered plausible within 3 to 6 months. It would be followed by several aftershocks, then a long tail of disclosures trickling out. The direct consequence: IT teams could face a “continuous-patching flood” — an unprecedented volume and pace of patches to deploy, without undermining operational continuity.
Two risks emerge immediately. First, software publishers may not be able to deliver patches fast enough. Second, internal teams (or integrators) may lack the bandwidth to absorb them, especially if patch implementation protocols are not aligned with the urgency.
What CISOs should do quickly
The Campus Cyber note is explicit about immediate operational priorities.
> Map critical assets and dependencies
Business supply chain and software supply chain: do you know precisely where potential vulnerabilities lie, including in open-source dependencies and third-party components?
> Simulate a massive wave of zero-days
A two-to-three-hour exercise bringing together the CTO, the CISO, and production leads to simulate the simultaneous discovery of around twenty zero-day vulnerabilities in a critical Internet-facing application. The objective: test the maturity of your entire vulnerability management chain, not just vulnerability by vulnerability.
> Harden the network architecture
Segmentation, reducing the “blast radius,” lowering the “mean time to remediaterem” in critical scenarios.
> Build an AI-augmented defense plan
There are many, often well-known use cases: automatic vulnerability triage, CVE prioritization based on actual exposure, generation and testing of patches, mapping software dependencies, detecting abnormal behavior, Level 1 and 2 SOC assistance, crisis simulations… They should be deployed as quickly as possible, under human supervision, favoring European or open-source solutions.
Often-forgotten collateral effect: the stress on teams
Even before any real attack, Mythos has already produced a tangible effect: a psychic overload on IT teams that are already structurally under pressure.
Asked to comment on a model they do not have access to, they must position themselves within a context that is uncertain, balancing their current roadmap with a hazy AI horizon.
The Campus Cyber describes this situation as a “true-false larval crisis,” akin to crisis management when the crisis is not yet fully real. Acknowledging this fact and not underestimating its impact on your teams is an immediate managerial responsibility.
The note also tackles a structural issue that goes beyond the technical perimeter: European companies are already more than 70% dependent on cybersecurity solutions that are not European. American dominance in AI will only deepen this dependency.
The sovereignty challenge is pressing for Europe
The dilemma is daunting: organizations seeking the best protection will have no choice but to rely on American AI. Those prioritizing technological autonomy take a real operational risk. Without a strong European alternative, the squeeze will be absolute.
The Campus Cyber sketches three axes for a response: strengthen French and European anticipatory capabilities, position Mistral AI and other European AI actors in cybersecurity use cases, and fully implement the AI Act and the Cyber Resilience Act, up to restricting the sale of models that do not meet the highest standards of transparency and safety.
Source: Campus Cyber Analytical Note “Mythos and other frontier models: implications of AI progress for cyber in France and Europe,” May 2026.
