LLMjacking and Advanced Jailbreaking in Financial Systems: Risks, Regulatory Impact in Brazil, and the Cognitive SOC Solution

The rapid adoption of large language models (LLMs) across financial institutions has introduced a new class of systemic cyber risk. In the era of AI symmetry—where attackers and defenders operate with comparable levels of automation and intelligence—traditional Security Operations Centers (SOCs) struggle to detect and contain attacks that exploit valid credentials, authorized APIs, and legitimate cloud services. This whitepaper examines two emerging and under-addressed threat classes in financial systems: LLMjacking, the hijacking of cloud-based LLM resources for large-scale inference or cost abuse, and advanced jailbreaking, the bypassing of ethical and safety guardrails to generate illicit content, manipulate financial models, or enable fraud. Drawing on documented real-world cases involving banks, fintechs, and insurers, the work demonstrates how these attacks generate significant financial loss, regulatory exposure, and systemic risk—often without triggering traditional security controls. With a specific focus on the Brazilian regulatory environment, the paper analyzes the implications of such attacks under BCB Resolution 4.658/2018, LGPD (Law 13.709/2018), CVM Resolution 35/2021, and Basel III / CMN frameworks, highlighting how failure to detect and mitigate AI-driven abuse may constitute governance and compliance violations. The core contribution of this work is the presentation of the Cognitive SOC as a preventive and adaptive security model. Central to this approach is Δ-Coherence, a quantitative measure of divergence between observed system behavior and expected operational semantics—even when actions are technically valid. By correlating multi-event behavior, interpreting intent, and applying progressive, proportional, and reversible containment through adaptive SOAR, the Cognitive SOC enables pre-impact detection of AI-driven abuse. The paper details the Δ-Coherence model, including scoring methodology, thresholds, and practical examples across AWS Bedrock, Azure OpenAI, and Google Vertex AI. It also provides a solution matrix illustrating how Cognitive SOC capabilities close critical gaps left by traditional security tools, and offers practical recommendations for financial institutions seeking to improve resilience against AI-enabled threats. This work positions Cognitive Security Operations as an essential evolution for financial cybersecurity, enabling regulatory compliance, cost containment, and systemic stability in an environment where behavioral misuse, not technical exploitation, defines the dominant attack surface.