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AI-powered applications are introducing new attack surfaces that traditional security models don’t fully capture, especially as these agentic systems gain autonomy. The guiding principle for the evolving attack surface is clear: Assume prompt injection. But turning that into effective defenses is rarely straightforward.
The Cyber Kill Chain security framework defines how attackers operate. At NVIDIA, we built the AI Kill Chain to show how adversaries compromise AI applications and demonstrate where defenders can break the chain. Unlike models that highlight attackers using AI, this framework focuses on attacks against AI systems themselves.
In this blog, we’ll outline the stages, show examples, and connect it to other security models.
Figure 1. NVIDIA AI Kill Chain: stages of an attack on AI-powered applications
Our AI Kill chain consists of five stages—recon, poison, hijack, persist, and impact—with an iterate/pivot branch. In the next sections, we’ll dive deeper into each stage of the AI Kill Chain, as shown above in Figure 1.
What happens during the recon stage of the AI Kill Chain?
In the recon stage, the attacker maps the system to plan their attack. Key questions an attacker is asking at this point include:
What are the routes by which data I control can get into the AI model?
What tools, Model Context Protocol (MCP) servers, or other functions does the application use that might be exploitable?
What open source libraries does the application use?
Where are system guardrails applied, and how do they work?
What kinds of system memory does the application use?
Recon is often interactive. Attackers will probe the system to observe errors and behavior. The more observability they gain into the system’s behavior, the more precise their next steps become.
Defensive priorities to break recon:
Access control: Limit system access to authorized users.
Minimize information: Sanitize error messages, scrub system prompts, guardrail disclosures and component identifiers from outputs.
Monitor for probing behaviors: Implement telemetry to detect unusual inputs or access patterns indicative of recon.
Harden models: Fine-tune models to resist transfer attacks and sensitive information elicitation.
Disrupting recon early prevents attackers from gaining the knowledge they need to execute precise attacks later in the Kill Chain.
How do attackers poison AI systems in this stage?
In the poison stage, the attacker’s goal is to place malicious inputs into locations where they will ultimately be processed by the AI model. Two primary techniques dominate:
Direct prompt injection: The attacker is the user, and provides inputs via normal user interactions. Impact is typically scoped to the attacker’s session but is useful for probing behaviors.
Indirect prompt injection: The attacker poisons data that the application ingests on behalf of other users (e.g., RAG databases, shared documents). This is where impact scales.
Text-based prompt infection is the most common technique. However, there are others such as:
Training data poisoning: Injecting tainted data into datasets used for fine-tuning or training models.
Adversarial example attacks: Bit-level manipulation of inputs (images, audio, etc.) to force misclassification.
Visual payloads: Malicious symbols, stickers, or hidden data that influence model outputs in physical contexts (e.g., autonomous vehicles).
Defensive priorities to break poison:
Sanitize all data: Don’t assume internal pipelines are safe, and apply guardrails to user inputs, RAG sources, plugin data, and API feeds.
Rephrase inputs: Transform content before ingestion to disrupt attacker-crafted payloads.
Control data ingestion: Sanitize any publicly writable data sources before ingestion.
Monitor ingestion: Watch for unexpected data spikes, anomalous embeddings, or high-frequency contributions to ingestion pipelines.
How do attackers hijack AI model behavior once poisoning succeeds?
The hijack stage is where the attack becomes active. Malicious inputs, successfully placed in the poison stage, are ingested by the model, hijacking its output to serve attacker objectives. Common hijack patterns include:
Attacker-controlled tool use: Forcing the model to call specific tools with attacker-defined parameters.
Data exfiltration: Encoding sensitive data from the model’s context into outputs (e.g., URLs, CSS, file writes).
Misinformation generation: Crafting responses that are deliberately false or misleading.
Context-specific payloads: Triggering malicious behavior only in targeted user contexts.
In agentic workflows, hijack becomes even more powerful. The elevated autonomy given to the LLM means that attackers can manipulate the model’s goals, not just outputs, steering it to execute unauthorized actions autonomously.
Defensive priorities to break hijack:
Segregate trusted vs. untrusted data: Avoid processing attacker-controlled and sensitive data in the same model context.
Harden model robustness: Use adversarial training, robust RAG, techniques such as CaMeL, or instruction hierarchy techniques to train models to resist injection patterns.
Validate tool calls contextually: Ensure every tool invocation aligns with the original user request.
Implement output-layer guardrails: Inspect model outputs for intent, safety, and downstream impact before use.
Hijack is the critical point where an attacker gains functional control. Breaking the chain here protects downstream systems, even if poisoning wasn’t fully prevented.
How do attackers persist their influence across sessions and systems?
Persistence allows attackers to turn a single hijack into ongoing control. By embedding malicious payloads into persistent storage, attackers ensure their influence survives within and across user sessions. Persistence paths depend on the application’s design:
Session history persistence: In many apps, injected prompts remain active within the live session.
Cross-session memory: In systems with user-specific memories, attackers can embed payloads that survive across sessions.
Shared resource poisoning: Attackers target shared databases (e.g., RAG sources, knowledge bases) to impact multiple users.
Agentic plan persistence: In autonomous agents, attackers hijack the agent’s goals, ensuring continued pursuit of attacker-defined goals.
Persistence enables attackers to repeatedly exploit hijacked states, increasing the likelihood of downstream impact. In agentic systems, persistent payloads can evolve into autonomous attacker-controlled workflows.
Defensive priorities to break persist:
Sanitize before persisting: Apply guardrails to all data before sending to session history, memory, or shared resources.
Provide user-visible memory controls: Let users see, manage, and delete their stored memories.
Contextual memory recall: Ensure memories are retrieved only when relevant to the current user’s request.
Enforce data lineage and auditability: Track data throughout its lifecycle to enable rapid remediation.
Control write operations: Require human approval or stronger sanitization for any data writes that could impact shared system state.
Persistence allows attackers to escalate from a single point-in-time attack to continuous presence in an AI powered application, potentially impacting multiple sessions.
How do attackers iterate or pivot to expand their control in agentic systems?
For simple applications, a single hijack might be the end of the attack path. But in agentic systems, where AI models plan, decide, and act autonomously, attackers exploit a feedback loop: iterate and pivot. Once an attacker successfully hijacks model behavior, they can:
Pivot laterally: Poison additional data sources to affect other users or workflows, scaling persistence.
Iterate on plans: In fully agentic systems attackers can rewrite the agent’s goals, replacing them with attacker-defined ones.
Establish command and control (C2): Embed payloads that instruct the agent to fetch new attacker-controlled directives on each iteration.
This loop transforms a single point of compromise into systemic exploitation. Each iteration reinforces the attacker’s position and impact, making the system progressively harder to secure.
Defensive priorities to break iterate/pivot:
Restrict tool access: Limit which tools, APIs, or data sources an agent can interact with, especially in untrusted contexts.
Validate agent plans continuously: Implement guardrails that ensure agent actions stay aligned with the original user intent.
Segregate untrusted data persistently: Prevent untrusted inputs from influencing trusted contexts or actions, even across iterations.
Monitor for anomalous agent behaviors: Detect when agents deviate from expected task flows, escalate privileges, or access unusual resources.
Apply human-in-the-loop on pivots: Require manual validation for actions that change the agent’s operational scope or resource access.
Iterate/pivot is how attackers influence compounds in agentic systems. Breaking this loop is essential to prevent small hijacks from escalating into large-scale compromise.
What kinds of impacts do attackers achieve through compromised AI systems?
Impact is where the attacker’s objectives materialize by forcing hijacked model outputs to trigger actions that affect systems, data, or users beyond the model itself.
In AI-powered applications, impact happens when outputs are connected to tools, APIs, or workflows that execute actions in the real world:
State-changing actions: Modifying files, databases, or system configurations.
Financial transactions: Approving payments, initiating transfers, or altering financial records.
Data exfiltration: Encoding sensitive data into outputs that leave the system (e.g., via URLs, CSS tricks, or API calls).
External communications: Sending emails, messages, or commands impersonating trusted users.
The AI model itself often cannot cause impact; its outputs do. Security must extend beyond the model to control how outputs are used downstream.
Defensive priorities to break impact:
Classify sensitive actions: Identify which tool calls, APIs, or actions have the ability to change external state or expose data.
Wrap sensitive actions with guardrails: Enforce human-in-the-loop approvals or automated policy checks before execution.
Design for least privilege: Tools should be narrowly scoped to minimize misuse, avoid multifunction APIs that broaden attack surface.
Sanitize outputs: Strip payloads that can trigger unintended actions (e.g., scripts, file paths, untrusted URLs).
Use Content Security Policies (CSP): Prevent frontend-based exfiltration methods, like loading malicious URLs or inline CSS attacks.
Robust downstream controls on tool invocation and data flows can often contain attacker reach.
How can the AI Kill Chain be applied to a real-world AI system example?
In this section, we’ll use the AI Kill Chain to analyze a simple RAG application and how it might be used to exfiltrate data. We’ll show how we can improve its security by attempting to interrupt the AI Kill Chain at each step.
Figure 2. Architecture diagram of a simple RAG application
An attacker’s journey through the AI Kill Chain might look something like this:
Recon: The attacker sees that three models are used: embedding, reranking, and an LLM. They examine open source documentation for known vulnerabilities, as well as user-facing system documentation to see what information is stored in the vector database. Through interaction with the system, they attempt to profile model behavior and elicit errors from the system, hoping to gain more information about the underlying software stack. They also experiment with various forms of content injection, looking for observable impact.
The attacker discovers documentation that suggests that valuable information is contained in the vector database, but locked behind access control. Their testing reveals that developer forum comments are also collected by the application, and that the web frontend is vulnerable to inline style modifications, which may permit data exfiltration. Finally, they discover that ASCII smuggling is possible in the system, indicating that those characters are not sanitized and can affect the LLM behavior.
Poisoning: Armed with the knowledge from the recon step, the attacker crafts a forum post, hoping it will be ingested into the vector database. Within the post they embed, via ASCII smuggling, a prompt injection containing a Markdown data exfiltration payload. This post is later retrieved by an ingestion worker and inserted into the vector database.
Mitigations: Publicly writable data sources such as public forums should be sanitized before ingestion. Introducing guardrails or prompt injection detection tools like NeMoGuard-JailbreakDetect as part of the filtering step can help complicate prompt injection through these sources. Adding filtering of ASCII smuggling characters can make it more difficult to inject hidden prompt injections, raising the likelihood of detection.
Hijack: When a different user asks a question related to the topic of the attackers forum post, that post is returned by the vector database and—along with the hidden prompt injection—is included in the augmented prompt sent to the LLM. The model executes the prompt injection and is hijacked: All related documents are encoded and prepared for exfiltration via Markdown.
Mitigations: Segregate trusted and untrusted data during processing. Had the untrusted forum data been processed separately from the internal (trusted) data in two separate LLM calls, then the LLM would not have had the trusted internal data to encode when hijacked by the prompt injection in the internal data. Rephrasing or summarization for each of the inputs might also have resulted in a higher level of difficulty in getting a prompt injection to operate.
Persist: In this case, persistence is automatic. As long as the poisoned data is retained in the vector database, additional queries will be able to trigger it.
Mitigations: Data segregation here, combined with careful audit logging of included sources, could reduce the blast radius of the attack, and allow for detection and removal of the tainted data.
Impact: When the LLM response, containing LLM-generated data exfiltration payload, is loaded by the victim’s browser in the RAG application frontend, it triggers a request to an attacker-controlled server. At this point, the data is exfiltrated and the attack (in this interaction) is complete.
Mitigations: Removing any URLs from the LLM produced output prevents many exfiltration attacks in this class. Content segregation (above) would prevent anything but untrusted data from being exfiltrated. Finally, strong content security policies on the front end can block requests to untrusted domains, while also blocking exfiltration
Because this application is only a “Level 1” agent with a deterministic process flow, the iterate/pivot step is not exercised.
Conclusion
Securing AI requires understanding how AI changes the attack surface, and building layered defenses that scale with autonomy levels. As enterprises deploy LLMs, RAG systems, and agentic workflows, attackers are actively evolving their methods to exploit these AI-driven systems.
The AI Kill Chain provides a clear, actionable way to break down how these attacks unfold—stage by stage. It helps teams move beyond generalized “prompt injection” concerns to see where, how, and why attackers can escalate their control. At NVIDIA, we operationalize many of these defenses through technologies like NeMo Guardrails, Jailbreak Detection NIMs, and architectural best practices.
Learn more about best practices for securing LLM-enabled applications, the framework for understanding agentic autonomy levels and security, and our NVIDIA AI red team.
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