AI-Data-Poisoning-KrishnaG-CEO

LLM04: Data and Model Poisoning – A C-Suite Imperative for AI Risk Mitigation

At its core, data poisoning involves the deliberate manipulation of datasets used during the pre-training, fine-tuning, or embedding stages of an LLM’s lifecycle. The objective is often to introduce backdoors, degrade model performance, or inject bias—toxic, unethical, or otherwise damaging behaviour—into outputs.

LLM-Integrity-KrishnaG-CEO

Secure System Configuration: Fortifying the Foundation of LLM Integrity

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

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.

Attack-Scenarios-Prompt-Injection-KrishnaG-CEO

🧠 Attack Scenarios and Risk Implications of Prompt Injection

Prompt injection is not just a vulnerability — it’s a multi-headed threat vector. From overt attacks to inadvertent leakage, each scenario introduces unique risks, requiring tailored strategies to safeguard operational integrity, regulatory compliance, and business reputation.