LLM Security in 2026

Your AI system just answered a question it was never supposed to answer. It extracted a confidential document, bypassed its safety controls, and sent the output somewhere you did not authorize. You did not know. Neither did your security team.

This scenario is not hypothetical. It reflects the documented reality of prompt injection attacks against enterprise LLM deployments in 2025 and 2026. Many organizations assumed their existing security stack already covered AI. It did not.

Today, 90% of enterprise organizations run large language models inside daily operations. Sales teams draft outreach with AI. Legal departments review contracts faster. Engineering teams embed LLMs directly into product pipelines.

Yet only 5% of organizations feel confident securing these systems.

The gap exists because LLM security does not align with traditional cybersecurity frameworks. Firewalls cannot stop prompt injection. Endpoint detection does not flag poisoned training datasets. The attack surface is new, tooling is still maturing, and threat actors are moving faster than enterprise defences.

This article explains the four attack vectors teams must understand in 2026, what the OWASP LLM Top 10 2025 requires enterprises to prioritise, and what practical LLM security best practices look like in real environments.

Attack Vector 1: Prompt Injection 

Prompt injection remains the number one attack vector in the OWASP LLM Top 10 2025. It embeds instructions inside inputs that a model interprets as commands instead of data.

In a direct attack, a crafted query overrides the system prompt and changes model behavior. In an indirect attack, malicious instructions hide inside retrieved content such as documents, webpages, or database records.

Attackers use this method to extract information from a model’s context window, bypass safety controls, or trigger downstream actions in agentic pipelines. As enterprises deploy AI agents that browse the web, read emails, and execute code, indirect prompt injection becomes a critical attack path.

Effective mitigation requires separating trusted instructions from untrusted input. Organizations should implement input validation layers, output filtering, and least-privilege design for agentic systems.

Attack Vector 2: Sensitive Information Disclosure 

A model that receives sensitive context in its system prompt can unintentionally reveal that information through carefully structured queries. This risk affects any deployment where the model accesses confidential data.

The threat expands in retrieval-augmented generation systems, where models pull documents from an internal knowledge base. Without access controls at the retrieval layer, users may extract documents they are not authorized to view simply by asking the right questions.

Strong controls include output filtering for personally identifiable information, role-based access controls at retrieval, and regular red-team exercises targeting data leakage paths. These measures form foundational LLM security best practices for enterprise deployments.

 

Attack Vector 3: Training Data Poisoning 

Training data poisoning introduces corrupted data into a dataset before or during fine-tuning. The result is a model that behaves incorrectly under attacker-controlled conditions, often without obvious warning signs.

For enterprises fine-tuning foundation models on internal data, risks include insider threats, compromised pipelines, and third-party datasets without verified provenance.

Security must begin earlier than most teams expect. Data provenance tracking, anomaly detection across training sets, and behavior benchmarking before and after fine-tuning provide the strongest defenses.

Attack Vector 4: LLM-jacking 

LLMjacking targets LLM API credentials. When attackers compromise an API key, they can run queries at the victim organisation’s expense, extract cached conversation data, and pivot into connected cloud services.

Because commercial LLM APIs operate on per-token pricing, a single exposed key can cause significant financial damage before detection occurs. Attackers typically obtain keys through exposed secrets in public repositories, phishing campaigns, or compromised developer environments.

Organizations should implement secrets scanning in CI/CD pipelines, API usage monitoring with anomaly thresholds, regular key rotation, and IP or service-identity restrictions.

 

OWASP LLM Top 10 2025: What Enterprises Must Address Now 

The OWASP LLM Top 10 2025 has become the closest reference to a compliance framework for enterprise AI cybersecurity. Security assessors, legal teams, and procurement leaders increasingly rely on it when evaluating AI vendors.

Beyond the four attack vectors discussed above, OWASP highlights insecure output handling, where model responses pass into downstream systems without sanitisation. It also addresses excessive agency, where models act with broad permissions and limited oversight.

Additional risks include model denial-of-service attacks caused by adversarial inputs and supply chain vulnerabilities affecting pre-trained models, plugins, and third-party data sources.

 

How The SamurAI Secures Your LLM Stack 

Most enterprise security teams specialize in traditional infrastructure. However, they are now responsible for securing systems that behave fundamentally differently.

The SamurAI AI Security for LLMs service is designed for this shift.

Our assessments evaluate the full attack surface, including prompt injection testing, data-flow mapping to identify sensitive exposure paths, training pipeline security reviews, API credential hygiene across LLM integrations, and architecture validation against the OWASP LLM Top 10 2025.

The result is a prioritised remediation roadmap your team can implement immediately.

Organizations that succeed are conducting structured assessments now, while remediation remains manageable. Those that delay will face significantly higher complexity and cost later.

Book a Free AI Security Assessment 

If your organisation is deploying LLMs and has not run a structured security assessment, book a free 30-minute AI Security Assessment with The SamurAI. We identify your highest-risk exposure points and give you a clear next step.  

 

No pitch. No obligation. Contact us today.