In late 2025, Anthropic disclosed that a state-linked hacking group misused its Claude AI tools to run an espionage campaign targeting companies and government agencies worldwide. In several cases, the attackers used Claude’s code-focused features to map infrastructure, identify valuable databases, generate exploit code, and facilitate the exfiltration of sensitive information. The AI handled much of the technical work, while the human operators focused on supervising and adjusting prompts.
This attack was not a theoretical red-team exercise. It was a live, AI-assisted intrusion effort, and a small number of targets were successfully compromised. For public sector leaders, this should be a watershed moment. The same category of tools governments are exploring for case management, digital services, and citizen engagement is also being used on the other side of the chessboard.
Marcman Solutions views this incident as a clear signal that public agencies need to refresh their approach to cybersecurity, AI governance, and operational resilience.
What Actually Happened, In Plain Terms
The attackers behind the Claude campaign did not rely on a single clever trick. They broke the problem into many smaller tasks that looked benign on the surface. They asked Claude to analyze network layouts, inspect configuration details, identify likely targets, such as high-value databases, and suggest ways to test them.
They then had the model generate and refine exploit code, walk through error messages, and optimize the attack until it worked. Once inside, the same tooling helped organize stolen data by type and apparent intelligence value.
At each step, the prompts were phrased to appear like legitimate security testing or software troubleshooting. That is how the attackers were able to slip past many of the guardrails built into the AI system. The provider ultimately detected the misuse, banned the accounts involved, and notified affected organizations, but only after the attackers had already demonstrated how far a determined actor could go.
For government officials, the technical details matter less than the strategic lesson. Capable actors can now treat powerful AI services as a co-pilot that accelerates reconnaissance, exploit development, and data theft.
Why This Is Different From Just Another Breach
Several characteristics of the Claude operation should be especially concerning for state and local agencies.
First, the barrier to entry is getting lower. Public reporting on this and similar attempts makes it clear that some malicious users lacked deep expertise in exploit development or malware engineering. They leaned on Claude for detailed step-by-step guidance, code generation, and troubleshooting. A moderately skilled adversary with a good prompt is now far more dangerous than they were two years ago.
Second, the pace of operations has changed. Tasks that used to require days of manual effort, such as reviewing logs, combing through documentation, or trial-and-error exploitation, can be compressed into minutes when offloaded to an AI that never gets tired and can run many experiments in parallel. That shift breaks many of the assumptions built into existing monitoring, escalation, and response processes.
Third, the AI service itself has become part of the attack surface. Prompt injection, jailbreaking, data exfiltration via model output, and abuse of features such as code execution and network access are now live risks, not hypothetical research topics.
Finally, the target set in this campaign included not only private industry but also government-related entities. Even if only a limited number of those targets were successfully compromised, the message is clear. Public institutions are now in scope for AI-enabled operations, and future campaigns will learn from these first iterations.
Why Public Sector Systems Are Particularly Exposed
Many public sector environments combine exactly the conditions that AI-augmented attackers find attractive.
Legacy platforms and decades-old applications are still in production in benefits administration, public health, transportation, and justice systems. They often run with limited segmentation, minimal modern identity protections, and irregular patching windows, because downtime is politically and operationally unacceptable.
Security controls are frequently fragmented across agencies, programs, and vendors. One department may have modern endpoint detection and response, while another relies on dated antivirus and manual log review. That inconsistency offers adversaries the opportunity to use AI for rapid reconnaissance and lateral movement, looking for the weakest point in a complex ecosystem.
Cyber teams in government are typically under-resourced. Even when agencies understand the risk, they may not have the capacity to redesign architectures, build robust AI governance processes, or continuously red-team their own systems with modern tools.
At the same time, the consequences of system failure are uniquely severe. Disruptions to unemployment systems, child welfare platforms, emergency management tools, or health data exchanges can have immediate and very human impacts. The Claude incident confirms that the tools needed to go after such systems are no longer confined to a handful of elite operators.
Four Practical Moves Leaders Can Make Now
Public sector leaders do not need to become AI researchers. Still, they do need to adjust their security strategy to assume that adversaries will use tools like Claude, GPT, and other advanced models.
First, update risk assessments to include AI-specific threats. Agencies should map where AI is already in use, where it is likely to be introduced through vendors and integrators, and how those models could be misused. This assessment includes examining prompt logging, model access paths, potential data leakage in prompts, and ways AI-assisted attackers might target existing systems more effectively.
Second, accelerate movement toward an authentic zero-trust architecture. Perimeter-centric security is increasingly misaligned with AI-enabled intrusion campaigns. Continuous verification, strong identity, granular access controls, and micro-segmentation matter even more when an automated system can quickly enumerate and probe every reachable service looking for a weak link.
Third, treat AI governance as part of cyber governance, not a separate compliance exercise. That means setting policies on which models may be used and with what kinds of data, defining how AI activity will be logged and monitored, and building AI-specific incident response playbooks.
Procurement language and vendor oversight should directly address model risk, misuse detection, and transparency obligations when AI is embedded in third-party solutions.
Fourth, use AI defensively instead of ceding the advantage. Agencies can adapt the same techniques that attackers are using. Models can accelerate log analysis, correlate signals across systems, support continuous configuration review, and enable automated threat hunting.
Structured correctly, AI can help small cyber teams keep pace with a much more automated adversary landscape.
Where Marcman Solutions Fits
Marcman Solutions focuses on helping public agencies make this transition in a disciplined, mission-aware way.
We support AI-informed risk and threat assessments that incorporate the specific lessons from the Claude campaign and similar incidents, rather than relying on generic templates. We design and manage zero-trust modernization efforts that respect the realities of legacy systems, procurement timelines, and federal and state compliance requirements. We help clients develop practical AI governance frameworks that align with national guidance and emerging federal directives while still allowing innovation.
On the operational side, we work with agencies to stand up AI-assisted defensive capabilities, from pilot projects that apply models to specific log sources through to broader programs that integrate AI into security operations, red teaming, and resilience planning.
Across all of this, our focus is on program execution. Technology is only one part of the response. Public sector organizations need clear accountability, realistic timelines, and a roadmap that acknowledges budget constraints and political context.
The Strategic Choice Facing Public Sector Leaders
The Claude campaign showed that sophisticated adversaries are no longer experimenting with AI at the margins. They are using it in real operations against real targets. Governments now face a choice. They can treat this as a one-off headline, or they can treat it as the moment they began designing for a world in which AI is a standard part of both attack and defense.
The agencies that move first will not eliminate risk, but they will significantly reduce the likelihood that an AI-enabled campaign turns into a prolonged outage or a public crisis. They will be in a position to adopt AI for their own missions because they will have built the governance and security foundations to use it responsibly.
Marcman Solutions is ready to help public sector organizations make that shift. If your agency is reassessing its cyber posture in light of the recent Claude incident, now is the time to define the next phase of your security and AI strategy, not after the next headline.