LLM security vulnerabilities are the exploitable weaknesses in large language models that allow attackers to bypass safety controls, extract training data, manipulate outputs, and abuse model capabilities. You face five primary vulnerability classes: prompt injection, training data extraction, jailbreaking, insecure output handling, and excessive agency granted to model-driven agents.
How LLM Security Vulnerabilities Differ from Traditional Software Flaws
Traditional software vulnerabilities produce deterministic, reproducible failures. A buffer overflow either works or it does not. LLM security vulnerabilities are probabilistic. The same prompt injection attack might succeed 8% of the time against one model version and 23% against another, making detection and patching fundamentally harder than conventional vulnerability management. OWASP’s 2025 Top 10 for LLM Applications ranked prompt injection as the number one risk for the second consecutive year, with 41% of reported LLM incidents traced to this single vector.
The attack surface is also far broader. A traditional application has defined inputs: form fields, API parameters, file uploads. An LLM processes natural language, meaning every word in every user message is a potential attack vector. Researchers at Carnegie Mellon demonstrated in 2025 that adversarial suffixes as short as 20 tokens could bypass safety alignment on GPT-4, Claude 3, and Llama 3 with success rates between 47% and 84%. You cannot write firewall rules for natural language the way you write them for network packets.
Prompt Injection: The Most Dangerous LLM Vulnerability
Prompt injection is the technique of crafting inputs that override an LLM’s system instructions, causing it to ignore safety guardrails or execute unintended actions. Direct injection occurs when a user explicitly instructs the model to disregard its programming. Indirect injection is more severe: an attacker embeds malicious instructions inside external content the LLM processes, such as hidden text in documents, manipulated API responses, or poisoned web pages. A detailed technical breakdown of this attack class is available in our guide to prompt injection attacks.
In September 2025, researchers demonstrated indirect injection attacks against AI email assistants that achieved an 88.3% success rate in extracting sensitive corporate data. Current state-of-the-art defences reduce attack success rates to approximately 2.1% on standard benchmarks, which still translates to thousands of successful exploits at enterprise scale.
LLM Vulnerability Types: Attack Vectors and Severity Ratings
| Vulnerability Type | OWASP LLM Rank | Attack Complexity | Detection Difficulty | Potential Impact |
|---|---|---|---|---|
| Prompt Injection (Direct) | #1 | Low | Medium | Guardrail bypass, unauthorised actions |
| Prompt Injection (Indirect) | #1 | Medium | Very High | Data exfiltration, remote code execution |
| Training Data Extraction | #6 | Medium | High | PII leakage, intellectual property theft |
| Insecure Output Handling | #2 | Low | Low | XSS, SQL injection via LLM output |
| Excessive Agency | #8 | Low | Medium | Unauthorised system access, data modification |
| Model Denial of Service | #4 | Low | Low | Resource exhaustion, service disruption |
| Jailbreaking | #5 | Low | High | Content policy bypass, harmful output generation |
Training Data Extraction and Model Inversion Attacks
Training data extraction attacks force an LLM to reproduce memorised content from its training set, including personally identifiable information, proprietary code, and copyrighted material. Google DeepMind’s 2025 research demonstrated that targeted prompting could extract verbatim training data from production LLMs at rates of 3.1 to 9.8 tokens per query attempt. For models trained on sensitive datasets, this creates direct GDPR and CCPA liability. Understanding the broader landscape of AI security risks helps you contextualise where data extraction fits among other threat vectors.
Model inversion is a related technique where an attacker reconstructs training data characteristics by analysing model outputs across thousands of queries. A 2025 study showed that membership inference attacks could determine with 91% accuracy whether a specific data record was included in a model’s training set, creating serious privacy implications for healthcare and financial LLM deployments.
Mitigating LLM Security Vulnerabilities in Production
Effective defence requires layered controls across input, processing, and output stages. Input sanitisation and prompt boundary enforcement reduce prompt injection success rates by 73%, according to testing by the AI Security Alliance. Output validation prevents insecure output handling by scanning LLM responses for code injection patterns, SQL fragments, and script tags before they reach downstream systems. Our guide to AI model safety covers alignment techniques and guardrail strategies that complement these technical controls.
Rate limiting and query logging are essential for detecting training data extraction attempts. Set alerting thresholds at 10,000 queries per hour per user for API-served models. Red team your LLM deployments quarterly using frameworks like OWASP’s LLM Testing Guide and Microsoft’s PyRIT toolkit. The EU AI Act, enforced since August 2025, mandates documented risk management for high-risk AI systems, with non-compliance penalties reaching 3% of global annual turnover.
Frequently Asked Questions
What are the most common LLM security vulnerabilities in production systems?
The most common LLM security vulnerabilities are prompt injection, insecure output handling, and excessive agency. OWASP’s 2025 LLM Top 10 reports that prompt injection alone accounts for 41% of documented incidents. Insecure output handling ranks second because developers frequently pass raw LLM outputs to downstream systems without sanitisation, enabling cross-site scripting and SQL injection.
How does a prompt injection attack compromise an LLM application?
A prompt injection attack overrides the LLM’s system instructions by embedding malicious directives in user input or external content the model processes. Successful attacks bypass safety guardrails, extract confidential data from the model’s context window, or trigger unauthorised actions through connected tools and APIs. Current defences reduce success rates to roughly 2% but cannot eliminate them entirely.
Can you fully patch LLM security vulnerabilities like traditional software bugs?
You cannot fully patch LLM security vulnerabilities using traditional methods because these flaws stem from the probabilistic nature of language models rather than deterministic code errors. Mitigations reduce attack success rates below acceptable thresholds, typically targeting under 1% for critical systems, but no current technique provides complete protection against prompt injection or training data extraction.
Read the complete guide: AI Security in 2026: Threats, Defences, and What Every Organisation Must Know