Security Threat Library

LLM Security Threats

A comprehensive encyclopedia of attack vectors targeting LLM applications. Understand how these threats work, see real examples, and learn prevention strategies.

15
Documented Threats
3
Threat Categories
15
OWASP LLM References

Prompt Attacks

Attacks that manipulate LLM behavior through crafted inputs

CriticalPrompt Attack

Prompt Injection

An attack where malicious instructions are embedded in user input to manipulate LLM behavior and bypass safety controls.

OWASP Reference: LLM01
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CriticalPrompt Attack

Jailbreak Attacks

Sophisticated prompts designed to bypass LLM safety guidelines and content policies to elicit harmful or restricted outputs.

OWASP Reference: LLM01
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CriticalPrompt Attack

Indirect Prompt Injection

Attacks where malicious instructions are hidden in external data sources that the LLM processes, rather than in direct user input.

OWASP Reference: LLM01
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HighPrompt Attack

Adversarial Prompts

Carefully crafted inputs designed to exploit model weaknesses, cause unexpected behaviors, or probe for vulnerabilities.

OWASP Reference: LLM01
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HighPrompt Attack

System Prompt Extraction

Techniques used to reveal the hidden system prompt, instructions, or configuration that defines an LLM application's behavior.

OWASP Reference: LLM07
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MediumPrompt Attack

Model Extraction

Attacks designed to steal or replicate an LLM's capabilities, weights, or behavior through systematic querying.

OWASP Reference: LLM10
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MediumPrompt Attack

Prompt Leaking

The unintended disclosure of conversation context, previous prompts, or multi-turn conversation history.

OWASP Reference: LLM06
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MediumPrompt Attack

Context Manipulation

Attacks that exploit or corrupt the LLM's context window to alter behavior or access unauthorized information.

OWASP Reference: LLM01
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LowPrompt Attack

Denial of Service (LLM)

Attacks designed to exhaust LLM resources, cause excessive costs, or make the service unavailable.

OWASP Reference: LLM04
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Data Leakage

Exposure of sensitive information through LLM outputs

Content Violations

Attempts to generate harmful, toxic, or policy-violating content

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