SentinelOne recently conducted a structured red teaming exercise on a real-world government education AI chatbot, referred to as EduBot. The system was designed to answer citizen questions about education policy, school enrollment, funding programs, and related public services. Like many production AI deployments in the public sector, it incorporated safety guardrails, content filters, and domain restrictions intended to keep responses helpful, accurate, and within approved boundaries.
The red team tested the chatbot against the OWASP Top 10 for Large Language Model Applications. What they discovered was concerning: despite having modern safety mechanisms, the system was vulnerable to several advanced prompt engineering techniques. The team successfully bypassed guardrails to extract the underlying system prompt, generate phishing emails, and produce content outside the bot’s intended domain.
These attacks did not rely on simple jailbreaks like DAN prompts or basic roleplay. Instead, they used more sophisticated methods including JSON encapsulation (framing malicious requests as “developer test data”), Base64 obfuscation, and compound multi-step attacks. The success of these techniques shows that current guardrail implementations often focus on blocking obvious malicious intent while remaining weak against structural and syntactic manipulation.
This case is particularly relevant because government chatbots are increasingly used for citizen services in education, healthcare, and benefits programs. When these systems can be manipulated to leak internal instructions or generate harmful content, it undermines public trust and creates real operational and compliance risks. The exercise serves as a clear warning: as more public sector organizations deploy agentic and conversational AI, the gap between “looks secure” and “actually secure” is becoming a significant governance issue.
The Setup And Why This Was Inevitable
Government agencies worldwide have accelerated the deployment of AI chatbots to improve citizen services, reduce administrative burden, and provide 24/7 access to information. Education departments have been among the earliest and most active adopters, using conversational AI to answer questions about school enrollment, funding programs, curriculum details, and administrative procedures.
EduBot was built with good intentions and standard safety measures. It included domain restrictions to keep responses focused on education topics, content filters to block harmful or inappropriate output, and prompt-level guardrails designed to prevent the system from going off-script. On paper, it looked like a responsible deployment of AI for public service.
However, like many production AI systems in 2026, its defenses were built primarily around semantic guardrails and refusal training. These mechanisms work reasonably well against obvious malicious prompts but often fail against more advanced, structured adversarial techniques. The red teaming exercise by SentinelOne revealed exactly this limitation in a real government environment.
The timing of this test is significant. As governments push harder to digitize citizen services and reduce costs, the pressure to deploy AI chatbots quickly has increased. Many agencies prioritize speed and user experience over deep adversarial testing. This creates fertile ground for the kinds of bypass techniques demonstrated here.
SentinelOne’s red team noted that the system performed adequately against basic jailbreak attempts, which likely gave developers and deployers false confidence in its robustness. This is a classic Pre-Failure Signal: the system looked secure against known, publicly documented attacks, but crumbled when faced with more sophisticated structural manipulation.
What Actually Happened
SentinelOne’s red team conducted a structured adversarial assessment of the government education chatbot (EduBot), specifically testing it against the OWASP Top 10 for Large Language Model Applications. The results revealed significant weaknesses in the system’s guardrails despite its production deployment and intended safety features.
The team succeeded in bypassing the chatbot’s protections using advanced prompt engineering techniques. Key successful methods included:
JSON encapsulation — Framing malicious requests as structured “developer test data” to bypass content filters.
Base64 obfuscation — Encoding harmful instructions to evade semantic detection.
Compound/multi-stage attacks — Gradually escalating requests to erode boundaries over multiple turns.
These techniques allowed the red team to achieve two major breakthroughs: extracting the underlying system prompt and generating content outside the bot’s sanctioned education domain, including phishing-style messages.
One member of the SentinelOne red team highlighted the deceptive nature of the success: the system would often respond as if operating normally, even while being steered into prohibited territory. The attacks succeeded not because basic guardrails were absent, but because they were primarily semantic and could be defeated through structural manipulation of the input format.
Importantly, EduBot performed relatively well against simple, well-known jailbreak patterns such as DAN-style prompts or basic role-playing scenarios. This partial resilience likely contributed to overconfidence in the system’s overall robustness. The gap between “secure against known attacks” and “secure against realistic adversarial techniques” proved to be substantial.
The Chain of Failure
The red teaming exercise revealed a clear chain of governance and technical failures that allowed the EduBot system to be compromised:
First, the system relied heavily on prompt-level guardrails and semantic filtering. These defenses were effective against simple, publicly known jailbreak techniques, giving the development team a false sense of security. However, they proved inadequate against structured, multi-step attacks that manipulated the format and context of the input.
Second, there was insufficient input validation and output sanitization. The system did not effectively verify whether incoming requests were legitimate educational queries or adversarial attempts disguised as structured data. Once the red team used JSON encapsulation and Base64 obfuscation, the guardrails failed to flag the manipulated input as suspicious.
Third, the system lacked runtime behavioral monitoring capable of detecting deviations from its sanctioned purpose in real time. Even when the chatbot began generating content outside its education domain or revealing internal instructions, there were no automated controls to interrupt or flag the behavior before the response was delivered.
Finally, the audit and logging mechanisms were not robust enough to provide clear visibility into how the system arrived at its responses. This made it difficult to reconstruct the full attack chain after the fact, weakening both incident response and compliance efforts.
This chain — over-reliance on surface-level prompt defenses, weak structural validation, missing runtime behavioral controls, and limited auditability — is a textbook example of governance theater in production AI systems. The system appeared secure during standard testing but collapsed under realistic adversarial pressure.
What It Actually Cost
Although this was a controlled red teaming exercise rather than a live malicious attack, the potential consequences of these vulnerabilities in a real government education system are significant and far-reaching.
A successful exploitation in production could have led to the leakage of internal system prompts, revealing how the chatbot was configured and potentially exposing other weaknesses. More critically, the ability to generate phishing emails or harmful content through a trusted government channel could erode public confidence in digital public services. Citizens expecting accurate, official information about school enrollment, funding, or education policy could instead receive misleading or malicious responses.
For the government agency involved, the fallout would include immediate reputational damage, increased scrutiny from oversight bodies, and potential regulatory investigations. In an era where governments are trying to build trust in digital services, a high-profile AI security incident could slow down broader digital transformation efforts and reduce citizen adoption of other online services.
Operationally, the agency would face the costly and time-consuming process of taking the system offline, conducting a full investigation, rebuilding guardrails, and re-testing — all while managing public communication about the breach. The indirect cost — lost trust in government AI initiatives — could be even more damaging in the long term
What Having It Right Would Have Looked Like
A properly governed version of EduBot would have looked fundamentally different from the system that was tested.
It would have started with a clear Agent Card — a live, versioned governance record defining the bot’s sanctioned purpose, permitted topics, data access boundaries, and escalation rules. Every response would have been evaluated against this card before being delivered to the user.
The system would have included a strong Agent Constitution — a set of inviolable principles encoded in the architecture (not just in prompts) that the bot could not reason around, such as never generating phishing content, never revealing internal instructions, and always staying strictly within education domain boundaries.
Input validation would have gone far beyond semantic filtering. It would have included structural checks for JSON encapsulation, Base64 obfuscation, and other known adversarial formatting techniques. A Tool Gateway layer would have mediated every potential action, verifying it against the agent card and constitution before execution.
Runtime monitoring would have been continuous and governance-aware — not just looking for threats or performance issues, but actively comparing the bot’s behavior against its sanctioned purpose and flagging deviations in real time. Human oversight would have been triggered for high-risk patterns rather than relying on periodic manual reviews.
Finally, comprehensive audit trails would have captured not only the final output, but the full reasoning path, input transformations, and decision context — making every interaction fully reconstructible for compliance and incident response purposes.
Our Take
AI Security Take
This SentinelOne red teaming exercise is a clear demonstration of why current approaches to AI security remain dangerously insufficient for production systems, especially in government and high-stakes environments.
The EduBot case shows that semantic guardrails and basic refusal training — still the foundation of many deployed AI systems — can be bypassed with relatively accessible structural and syntactic techniques. The fact that the system performed adequately against simple jailbreaks but collapsed under JSON encapsulation and compound attacks highlights a common problem: security teams are often defending against yesterday’s threats while today’s adversaries are already moving to more sophisticated methods.
For security leaders, the takeaway is urgent. Relying primarily on prompt-level filters and post-generation content moderation creates a false sense of security. Real AI security in the agentic era requires deeper architectural controls: robust input validation, structural anomaly detection, runtime behavioral monitoring against defined constraints, and strong output sanitization before responses reach users.
The EduBot incident should serve as a wake-up call. Government agencies and enterprises deploying conversational or agentic AI systems cannot treat security as an add-on. It must be designed into the system from the beginning, with continuous validation that goes far beyond traditional prompt engineering.
