Security Risks in Language Models (LLMs)

2026-05-22 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Intermediate, short

Summary

Large Language Models (LLMs) such as ChatGPT, Gemini, Claude, and Llama, despite their widespread utility, introduce significant security risks across their lifecycle. During use, "jailbreaking" circumvents safety barriers with over 90% success against models like ChatGPT and Llama-2, enabling harmful content generation. Prompt injection, similar to SQL injection, exploits the LLM's inability to distinguish commands from content, leading to direct attacks or indirect propagation via malicious web content, exemplified by the Morris-II email worm. Data leakage is also a concern, as LLMs can memorize and reveal private data from training. During training, data poisoning and backdoors can implant malicious behaviors, with studies showing 100 examples during fine-tuning can be sufficient. Supply chain risks involve compromised models from platforms like Hugging Face, evidenced by PoisonGPT and 1,600 leaked API tokens. Additionally, criminals use specialized LLMs like WormGPT and FraudGPT for phishing and malware, with AI agents autonomously exploiting zero-days.

Key takeaway

For MLOps Engineers or AI Security Engineers deploying LLMs, you must recognize their distinct attack surface, vulnerable to conversational manipulation and data exfiltration. Implement multi-layered defenses, including robust input filtering and strict access controls for AI agents. Treat all LLM output as potentially untrustworthy, especially when connected to internal systems. Prioritize verifiable model provenance to mitigate supply chain risks. Regularly audit interactions and never expose sensitive data to public models.

Key insights

LLMs face unique security threats from manipulation, data leakage, and supply chain vulnerabilities across their lifecycle.

Principles

• LLMs cannot naturally distinguish commands from content.
• Security training can be circumvented by ingenious prompts.
• Treat LLM output as untrustworthy content.

Method

Security in LLMs combines input filtering, differential privacy for training data, anomaly detection, cleansing fine-tuning, and model signing for verifiable provenance.

In practice

• Never paste secrets into public chatbots.
• Isolate AI agents with minimal permissions.
• Audit LLM interaction logs regularly.

Topics

• LLM Security
• Prompt Injection
• Jailbreaking
• Data Poisoning
• Supply Chain Security
• AI Agents
• Data Leakage

Best for: AI Security Engineer, MLOps Engineer, AI Engineer

Related on AIssential

• Security and Privacy of Large Language Models: Threat Taxonomy, Ethical Implications, and… — LLM risks are systemic, requiring a lifecycle-oriented defense-in-depth strategy across technical, human, organizational, and governance layers.
• OWASP's Top 10 Ways to Attack LLMs: AI Vulnerabilities Exposed — The OWASP Top 10 for LLMs highlights critical, evolving security threats from prompt injection to supply chain vulnerabilities.
• Yet another experiment proves it's too damn simple to poison large language models — LLMs with web search are vulnerable to simple, low-cost data poisoning via fabricated online sources.
• CNCF Warns Kubernetes Alone Is Not Enough to Secure LLM Workloads — Kubernetes provides infrastructure security but lacks AI-specific controls for LLM behavior and semantic risks.
• Using LLMs to secure source code — LLMs streamline vulnerability discovery, shifting the bottleneck to verification, triage, and patching.
• The 3 Invisible Risks Every LLM App Faces (And How to Guard Against Them) — LLM applications introduce unique security risks requiring specialized guardrails beyond traditional software security.

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.