LLM risk mitigation

LLM-MisInfo-KrishnaG-CEO

LLM09:2025 Misinformation – The Silent Saboteur in LLM-Powered Enterprises

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In the digital-first world, Large Language Models (LLMs) such as OpenAI’s GPT series, Google’s Gemini, and Anthropic’s Claude are redefining how businesses operate. From automating customer service and accelerating legal research to generating strategic reports, LLMs are integrated into critical enterprise workflows.
LLM misinformation occurs when the model generates false or misleading information that appears credible. This is particularly dangerous because of the model’s inherent linguistic fluency—users often assume that well-phrased responses are factually correct.

LLM-Integrity-KrishnaG-CEO

Secure System Configuration: Fortifying the Foundation of LLM Integrity

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When deploying LLMs in enterprise environments, overlooking secure configuration practices can unintentionally expose sensitive backend logic, security parameters, or operational infrastructure. These misconfigurations—often subtle—can offer attackers or misinformed users unintended access to the LLM’s internal behaviour, leading to serious data leakage and system compromise.

LLM-Sensitive-Info-KrishnaG-CEO

OWASP Top 10 for LLM – LLM02:2025 Sensitive Information Disclosure

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While theoretical risks highlight potential harm, real-world scenarios bring the dangers of LLM02:2025 into sharper focus. Below are three attack vectors illustrating how sensitive information disclosure unfolds in practical settings.