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Large Language Models are increasingly embedded into enterprise applications, APIs, and operational workflows. They process user input, documents, and system data at runtime, often with access to internal tools and downstream systems.
OWASP has identified prompt injection as LLM01, the highest priority risk in its LLM Top 10. This placement reflects how frequently prompt injection appears across real world LLM deployments and how broadly it impacts confidentiality, integrity, and control.
OWASP’s classification highlights a structural issue rather than an implementation flaw. Prompt injection does not rely on exploiting a vulnerability in the model itself. It arises because LLMs are designed to follow instructions, and those instructions may originate from untrusted sources at runtime.
In enterprise environments, this creates a security gap. LLMs may consume input from users, documents, APIs, or integrated tools without a reliable mechanism to distinguish trusted instructions from adversarial ones. When this occurs, the system behaves as designed, but outside intended control boundaries.
Prompt injection therefore represents a failure of execution control, not a failure of prompt design.
What Is Prompt Injection?
Prompt injection is a class of attacks in which untrusted input alters the behavior of a Large Language Model by influencing or overriding its intended instructions.
OWASP defines prompt injection as a risk where attacker controlled content is interpreted as authoritative instructions by the model. This content may come directly from a user or indirectly through data sources that the model is instructed to process.
Prompt injection differs from hallucination. Hallucination involves incorrect or fabricated outputs without malicious intent. Prompt injection involves deliberate manipulation of model behavior through crafted input.
It also differs from misuse. Misuse assumes the user is operating within allowed functionality. Prompt injection exploits ambiguity in instruction handling to bypass intended constraints.
Under OWASP LLM01, prompt injection is treated as an integrity and control failure. The model continues to operate normally, but the trust boundary between instructions and data is violated.
How Prompt Injection Works in LLM Systems
LLM based systems typically operate with multiple layers of instruction. These include system level prompts, developer defined prompts, and runtime input derived from users or external data.
Prompt injection occurs when untrusted input is interpreted at the same authority level as trusted instructions. This can happen when the model is asked to process content without clear separation between instruction and data.
Indirect prompt injection is common in enterprise use cases. Models are instructed to summarize documents, analyze emails, query APIs, or interact with tools. Malicious instructions embedded in those sources can influence the model’s behavior without direct user interaction.
Because LLMs do not have inherent awareness of trust boundaries, they process input based on contextual patterns rather than security semantics. The model does not “know” which instructions should be ignored unless explicit controls are enforced at runtime.
The result is behavior that appears coherent and intentional, even when it violates policy or operational constraints.
Common Types of Prompt Injection Attacks
Direct Prompt Injection
Direct prompt injection occurs when an attacker supplies input that explicitly instructs the model to ignore prior constraints or perform unauthorized actions. This input is provided directly through user facing interfaces.
Indirect Prompt Injection
Indirect prompt injection occurs when malicious instructions are embedded in data the model is instructed to process. Examples include documents, web pages, emails, or API responses.
Instruction Override and Role Confusion
Attackers may attempt to redefine the model’s role or authority by embedding instructions that conflict with system or developer prompts. The model may follow the most recent or contextually dominant instruction.
Data Exfiltration via Model Output
Prompt injection can be used to extract sensitive information by instructing the model to reveal internal context, prior inputs, or data it was not intended to expose.
Tool and Function Misuse
In systems where LLMs invoke tools or functions, prompt injection can manipulate tool usage. This may result in unauthorized API calls, data modification, or unintended automation behavior.
Each of these attack types exploits the same underlying condition: the absence of reliable control over how instructions are interpreted at runtime.
Why Prompt Injection Is Hard to Detect
Prompt injection is difficult to detect because it does not resemble traditional exploitation patterns. The system continues to function normally, and outputs often appear coherent, relevant, and contextually appropriate.
There is no fixed signature that distinguishes malicious instructions from legitimate input. Prompt injection operates through natural language, which is also the primary interface for normal interaction with LLM systems. This makes static pattern matching ineffective.
Detection is further complicated by context dependence. The same input may produce different behavior depending on prior prompts, conversation history, or external data sources. This variability reduces the reliability of deterministic detection methods.
In many cases, injected behavior aligns with the model’s expected capabilities. The model may summarize, extract, or act on information exactly as instructed. From an execution standpoint, nothing appears broken. The failure lies in control, not correctness.
Indirect prompt injection increases the challenge. When instructions are embedded in documents, emails, or API responses, the source of the behavior is separated from the interface through which the model is invoked. Tracing causality becomes difficult without visibility into the full execution context.
Tool using systems add another layer of complexity. When models invoke functions or APIs, the boundary between reasoning and action blurs. Prompt injection may influence downstream systems without producing obviously suspicious output at the model layer.
Prompt injection remains difficult to detect because it exploits how LLMs are designed to operate. Without visibility into how inputs, instructions, and actions interact at runtime, malicious behavior blends into normal execution.
What Prompt Injection Enables in production
Prompt injection becomes a security concern because of what it enables once LLMs are integrated into real systems. The impact extends beyond model output and into data access, automation, and decision making.
1. Exposure of Sensitive Information
LLMs often process internal documents, configuration data, or contextual memory as part of their operation. Prompt injection can influence how this context is used, leading to the disclosure of information that was not intended to be exposed. This may include internal instructions, embedded system context, or sensitive data introduced through prior interactions.
2. Unauthorized Actions Through Tool Invocation
In systems where LLMs are connected to tools or functions, prompt injection can alter how those tools are used. The model may be instructed to invoke APIs, execute workflows, or perform operations outside its intended scope.
These actions may be syntactically valid and operationally successful, even though they violate policy or governance constraints.
3. Manipulation of Downstream Systems
LLMs are increasingly used as intermediaries between users and backend systems. Prompt injection can influence how requests are generated, modified, or sequenced before being passed downstream. This creates opportunities for logic manipulation that do not require direct access to the underlying systems.
4. Policy and Compliance Violations
Prompt injection can cause LLMs to process or disclose data in ways that conflict with organizational policy or regulatory requirements. Because the model continues to function correctly, these violations may not be immediately apparent.
Audit trails and control assurances become unreliable when execution behavior cannot be traced back to trusted instructions.
5. Loss of Trust in Automated Decisions
When LLM output is used to support or automate decisions, prompt injection can influence outcomes without leaving clear evidence of tampering. This undermines confidence in systems that rely on LLM reasoning as part of operational workflows.
Prompt injection does not require breaking the model. It requires influencing how the model interprets input in environments where LLM output carries operational consequence.
Why Traditional Security Controls Miss Prompt Injection
Traditional security controls are designed to protect systems where intent and execution are clearly separated. Prompt injection operates in environments where that separation does not exist.
1. Input Validation Is Insufficient
Input validation assumes that malicious input can be distinguished from legitimate input based on structure, format, or known patterns. Prompt injection uses natural language, which is also the expected input format for LLM systems.
Malicious instructions do not appear malformed. They are grammatically valid and contextually relevant, making them indistinguishable from normal interaction at the validation layer.
2. Perimeter and Access Controls Do Not Apply
Network based controls such as WAFs, gateways, and access controls focus on who can reach a system and through which interfaces. Prompt injection does not require unauthorized access to the system.
Attackers operate within permitted channels, supplying input that the system is explicitly designed to accept. Perimeter controls remain intact while execution behavior changes internally.
3. Static Testing Cannot Model Runtime Context
Traditional security testing relies on predictable inputs and repeatable execution paths. Prompt injection depends on context, conversation state, and external data sources that change over time.
Static analysis and pre deployment testing cannot account for how instructions interact dynamically during execution, particularly when models process untrusted documents or tool responses.
Logging Lacks Semantic Context
Logs typically capture requests, responses, and error conditions. They do not capture how instructions were interpreted or why a particular output was produced.
Without visibility into instruction flow, it is difficult to determine whether an output resulted from intended behavior or injected influence.
Security Controls Assume Deterministic Behavior
Many security mechanisms assume that systems behave deterministically when given the same input. LLM systems do not operate under this assumption.
Prompt injection exploits variability in how language is interpreted across contexts. Controls that depend on predictable execution struggle to identify abnormal behavior.
Traditional security controls miss prompt injection because they are applied outside the decision making layer. Prompt injection occurs within the execution logic of LLM systems, where intent and data converge.
Why Runtime Controls Are Required
Prompt injection cannot be addressed solely at design time because it occurs during execution. Controls that operate before deployment do not observe how inputs, instructions, and external data interact once the system is running.
LLM systems consume input continuously. User queries, documents, API responses, and tool outputs are introduced at runtime. Any of these inputs can influence model behavior if instruction boundaries are not enforced during execution.
Runtime controls focus on observing and validating behavior as it happens. They provide visibility into the inputs the model processes, the instructions it follows, and the actions it triggers. This visibility is necessary to distinguish intended behavior from injected influence.
Because prompt injection is context dependent, detection must account for execution state. The same input may be benign in one context and harmful in another. Runtime controls can evaluate behavior in relation to prior prompts, system instructions, and tool interactions.
Runtime controls also operate continuously. They can detect changes in behavior over time, including shifts in how models respond to similar inputs or how tools are invoked. This is particularly important in systems where LLM behavior evolves through updates, configuration changes, or expanded integrations.
Without runtime controls, prompt injection remains invisible to security teams. With runtime controls, execution behavior can be monitored, analyzed, and constrained while the system operates.
How Levo Enables Runtime AI Security
Levo enables runtime AI security by observing how Large Language Models operate during execution and correlating that behavior with security and governance context. The approach focuses on execution visibility rather than declared intent, prompt structure, or pre deployment controls.
AI Runtime Visibility
Levo provides visibility into LLM execution as it occurs. This includes observing model inputs, instruction context, and outputs generated during operation. Runtime visibility allows security teams to understand what information the model processes and how it responds under real conditions, including when input originates from users, documents, APIs, or integrated tools.
AI Threat Detection
Levo applies threat detection to LLM execution behavior. Detection focuses on identifying patterns that indicate potential abuse, including instruction manipulation, unexpected output behavior, or anomalous interactions with downstream systems. Threat detection is based on observed behavior rather than static rules or prompt inspection.
AI Attack Protection
Levo supports protection mechanisms that operate during execution. When risky or policy violating behavior is detected, controls can be applied to limit impact, restrict actions, or prevent further execution. Protection is tied to runtime signals rather than assumptions about how prompts should behave.
AI Monitoring and Governance
Levo monitors LLM behavior over time to support governance and oversight. Monitoring captures how models are used, how often certain behaviors occur, and how execution patterns change as systems evolve. This enables alignment between governance expectations and actual model usage in production environments.
AI Red Teaming
Levo supports AI red teaming by enabling controlled testing of LLM behavior under adversarial conditions. These exercises help organizations understand how prompt injection and related risks manifest during execution.
Red teaming is grounded in observing runtime behavior rather than theoretical attack models. Through runtime visibility, detection, protection, monitoring, and testing, Levo enables organizations to address prompt injection as an execution control problem rather than a prompt design challenge.
Conclusion: Prompt Injection Requires Runtime Awareness
Prompt injection persists because it operates within expected system behavior. The model responds coherently. The application continues to function. The failure occurs in control rather than correctness.
Design-time safeguards and prompt hygiene do not provide sufficient protection once LLMs consume untrusted input at runtime. As LLMs are integrated into applications, workflows, and tools, security depends on observing and constraining execution behavior as it happens.
Prompt injection is therefore not an AI novelty risk. It is a structural security issue that requires runtime visibility, detection, and enforcement to manage effectively.
