Source: veeam.com/blog
A new article from Veeam experts explores 10 major threats to AI infrastructure caused by cyberattacks and misconfigurations of LLM-based assistants. The Veeam platform, particularly the new Agent Commander solution, provides protection against GenAI incidents, helping to mitigate or fully eliminate prompt injections, data poisoning, index compromise, prompt leaking, and excessive agent costs. Wise IT is an official Veeam partner specializing in implementing company solutions into organizational infrastructures.
Why it matters right now
Enterprises are moving beyond basic chatbots and deploying LLM-powered assistants capable of:
- Retrieving information from internal repositories (RAG).
- Surfacing sensitive content.
- Creating tickets and triggering workflows.
- And most importantly: performing actions through tool integrations (e.g., email, ITSM, IAM, cloud APIs, DevOps pipelines).
This is where the nature of risk changes dramatically.
Traditional application security focuses on code execution paths and APIs. However, LLM-based applications add a second, less predictable layer: natural language instructions that can be manipulated—sometimes directly by a user, and sometimes indirectly through the content the system retrieves (e.g., documents, web pages, tickets, PDFs, wiki pages).
In practice, GenAI security is not just about “model safety.” It becomes an intersection of:
- Data governance (simply put, what the AI can access and provide to the user) — the domain of platforms like Securiti AI (e.g., discovery, classification, access rights, policy enforcement).
- Cyber resilience (how quickly you can recover from a failure) — the domain of platforms like Veeam (e.g., immutable backups, clean restores, rolling back changes, recovery assurance).
This blog post covers 10 common LLM attacks you should prepare for and provides an enterprise checklist your team can use as a pre-release validation stage for AI solutions.
The new LLM attack surface in one image
Most LLM incidents aren’t the result of a model turning “evil.” They are usually caused by one or more of these realities:
- The model treats untrusted text (from users or retrieved documents) as instructions.
- The application passes too much context (including sensitive data) into prompts.
- Tools and agents are given excessive permissions.
- The system assumes LLM output is trustworthy and executes it.
- The organization lacks rollback and clean recovery mechanisms in case of interference with the AI pipeline or knowledge base.
10 Common LLM Attacks: Business Impact and Mitigation
1) Prompt Injection (Direct and Indirect)
Definition: An attacker manipulates the model’s behavior using specially crafted instructions.
- Direct Injection: An attacker enters malicious instructions directly into the chat.
- Indirect Injection: Malicious instructions are hidden within content retrieved by your system (e.g., wiki page, PDF, ticket comment) and reach the model via RAG.
Business Impact
- Unauthorized disclosure (“summarize the sensitive folder”).
- Unauthorized actions (“create a user,” “reset MFA,” “send this email”).
- Policy bypass (“ignore restrictions and proceed”).
Mitigation
- Treat all user input and retrieved content as untrusted.
- Maintain strict separation between system instructions and retrieved content in your orchestration logic.
- Use tool whitelisting and argument validation so the model never has unchecked authority.
- Apply the principle of least privilege at the retrieval and tool layers.
2) Sensitive Information Disclosure (Data Leakage)
Definition: The model outputs data it shouldn’t because it was in the prompt/context, retrieved without proper authorization, present in logs, or available through separate tools.
Business Impact
- Leakage of personal data (PII/PHI/PCI).
- Leakage of secrets (API keys, tokens, credentials).
- Loss of confidential intellectual property (roadmaps, pricing, deal materials).
Mitigation
- Discover and classify sensitive data before connecting it to AI. Data governance comes first.
- Implement retrieval permissions based on identity and access rights, not just “whoever asked the bot.”
- Set up output filtering/redaction for regulated data classes.
- Remove secrets from prompts and agent instructions; use dynamic retrieval from secure vaults instead.
3) Supply Chain Attacks (Models, Tools, Libraries, Plugins, etc.)
Definition: A threat enters through third-party components: models, libraries, plugins, prompt templates, connectors, or datasets.
Business Impact
- Unauthorized disclosure (“provide information from the confidential folder”).
- Unauthorized actions (“create a user,” “reset MFA,” “send this email”).
- Policy bypass (“ignore restrictions and proceed”).
Mitigation
- Maintain an inventory of AI components and versions (models, embeddings, tools, connectors).
- Vet third-party plugins/connectors and limit their scope.
- Version and protect prompt templates and configurations as production assets.
- Ensure rapid rollback capability if a compromise is detected.
4) Data Poisoning: Training, Fine-Tuning, RAG Corpus
Definition: Attackers manipulate the data the system learns from or retrieves. This applies to training sets, fine-tuning data, feedback loops, or RAG content.
Business Impact
- Persistent misinformation, such as: “always recommend unsafe steps.”
- Hidden policy bypass embedded within “trusted” content.
- Long-term integrity breaches affecting multiple users.
Mitigation
- Apply data lineage controls: who, when, and where content was added or modified.
- Separate high-trust and low-trust sources when ingesting into RAG.
- Implement approval workflows for updates to authoritative knowledge bases.
- Store immutable versions of corpora and indices for rollback to a verified state.
5) Improper Output Handling (LLM output as an injection vector)
Definition: Downstream systems treat LLM output as trusted and render it as HTML, execute it as code, or use it to build DB queries or API calls without validation.
Business Impact
- XSS or UI injections.
- Command injections or SQL-style injection consequences.
- Tool abuse via malicious arguments.
Mitigation
- Treat LLM results as user input: validate, escape, and sanitize.
- Use structured outputs (schemas) and server-side verification.
- Never execute “generated code” without sandboxing and approval steps.
6) Excessive Autonomy through Overprivileged Agents
Definition: Agents are granted broad permissions and autonomy, turning a single injection or error into a massive problem.
Business Impact
- Automated destructive changes, such as data deletion or configuration corruption.
- Rapid error propagation in systems like email, IAM, ITSM, and cloud.
- High-speed data exfiltration.
Mitigation
- Implement bounded autonomy by default (approvals for risky actions).
- Require additional authentication for critical operations.
- Limit impact radius using short-lived tokens and minimal access rights.
- Log and audit tool calls; treat agents as privileged accounts.
7) System Prompt Leakage (Exposing Instructions and Policies)
Definition: Attackers trick the model into revealing system prompts, tool instructions, or hidden policy logic to facilitate future attacks.
Business Impact
- Makes bypassing guardrails easier as hackers learn “how the model thinks.”
- Exposure of internal workflows and endpoints.
- Leaking proprietary prompt engineering and business logic.
Mitigation
- Do not keep secrets in prompts.
- Store sensitive instructions as protected assets with RBAC.
- Avoid over-detailing prompts and never include internal passwords or addresses in the text.
- Monitor for repeated attempts to extract system instructions.
8) RAG / Vector Store Vulnerabilities (Attacks on Embeddings and Search)
Definition: Attacks on the embedding pipeline, vector database, and search logic, especially in multi-tenant environments.
Business Impact
- Data leakage between different tenants.
- Indirect prompt injection via planted data fragments.
- Persistent presence of malicious content in indexing.
Mitigation
- Protect the vector store like a production database (network controls, auth, encryption).
- Ensure tenant isolation and document-level permission checks during search.
- Develop rapid purge and re-indexing procedures.
- Maintain versioned backups of indices and ingestion configs.
9) Misinformation (Hallucinations and Fact Manipulation)
Definition: The model provides plausible but incorrect answers or relies on manipulated or “poisoned” sources.
Business Impact
- Incorrect troubleshooting instructions leading to downtime.
- Compliance and legal risks due to fabricated citations.
- Faulty decisions made with unearned confidence.
Mitigation
- Require grounding only in approved sources for critical processes.
- Show citations, source snippets, and metadata wherever possible.
- Keep humans in the loop for actions involving security, legal, finance, or customer commitments.
- Secure and govern your official runbooks.
10) Unbounded Consumption (Resource Exhaustion and Costs)
Definition: Attackers or runaway processes cause a surge in token usage, tool calls, or search cycles, leading to downtime or massive bills.
Business Impact
- Service degradation similar to a DoS attack.
- Budget overruns.
- Cascading failures due to rate-limiting of external APIs.
Mitigation
- Implement rate limits, token caps, and tool-call quotas.
- Use circuit breakers and loop detection for agents.
- Set hard budgets for specific projects and automated spending alerts.
Want to securely implement AI agents in your infrastructure or set up reliable data recovery? Contact the Wise IT team — an official Veeam partner and leading AI solution provider! Our experts will provide a free consultation and share IT best practices for your industry:
Enterprise Approach: Govern AI Access and Recover What It Breaks
Most GenAI programs focus on “safe prompts.” While necessary, this is not enough.
Production readiness combines:
- Data Governance and Access Rights: discover, classify, restrict access, and monitor usage.
- Resilience and Recovery Readiness: immutable backups, clean recovery points, and tested restoration of systems and data that your AI depends on.
When working with LLM applications, a realistic goal isn’t zero incidents, but rather:
- Minimal Blast Radius
- Rapid Detection
- Instant Rollback and Clean Recovery
- Auditability and Proof of Control
How Veeam Helps Protect AI Infrastructure
The Veeam platform and its new Agent Commander solution, announced in February 2026 following the acquisition of Securiti AI, provide resilience and an “undo button” for GenAI incidents. This allows organizations to minimize risks from prompt injections, data poisoning, index compromise, prompt leaking, and excessive agent consumption costs.
Key GenAI Protection Tools
- Immutable Backups: protecting RAG corpora, vector databases, and prompts from data poisoning and unauthorized modifications.
- Clean Restore: rapidly returning the AI stack to a guaranteed safe state after prompt injections or breaches.
- Versioned Copies: enabling precise rollbacks of indices, embeddings, and settings to pre-incident versions.
- Configuration and Metadata Protection: backing up system prompts, agent instructions, and orchestration workflows.
- DR Orchestration: automated recovery of the entire AI infrastructure with a minimal blast radius and integrity verification.
- K8s and Linux Protection: securing the underlying environments (Veeam + Kasten) where models, pipelines, and vector services are deployed.
- Security Policy Redundancy: protecting IAM roles, secrets, and tokens to prevent agent privilege manipulation.
- Audit and Validation (SureBackup): automatic “cleanliness” and health checks of copies before they are used.
Final 10-Step LLM Security and Resilience Checklist
Use this as a final validation for any LLM application, RAG assistant, or agentic workflow.
- Perform a full AI component inventory
- Apps, models, prompts, workflows, tools, connectors, data sources, vector DBs
- Assign an owner to every system
- Discover and classify data before the AI touches it
- Identify PII/PHI/PCI, secrets, and critical IP
- Separate data into allowed and forbidden for AI
- Implement Least Privilege for retrieval and actions
- Retrieval must respect user identity and document permissions
- Limit tool permissions with short-lived tokens and whitelists
- Secure against Direct and Indirect prompt injections
- Treat all incoming content as untrusted
- Add policy filters before retrieval and before outputting responses
- Limit tool usage and agent autonomy
- Approve specific endpoints and validate arguments
- Require confirmation for actions like “delete,” “pay,” or “publish”
- Treat LLM output as untrusted
- Validate/escape data before execution or display
- Use structured schemas and server-side verification
- Protect the RAG layer and vector DB as production environments
- Control data lineage and track changes
- Ensure data isolation and encryption
- Log and monitor AI-specific signals
- Prompts (redacted), retrieval hits, tool calls
- Detect anomalies: injection patterns, activity spikes, unusual search volumes
- Prepare “kill switches” and response plans
- Ability to instantly disable tools or isolate an agent
- Key rotation procedures for all connected services
- Resilience: Immutable backups and clean restores
- Back up source data, configs, prompts, and indices
- Regularly test recovery for rapid rollback to a known good state
Final Thoughts
LLMs are becoming the new natural language “interface” to enterprise systems, often handling sensitive data and possessing the authority to execute actions. Success belongs to teams that view AI security as a comprehensive discipline: govern access, bound actions, verify outputs, and guarantee recovery. If you can consistently meet these four conditions, you can safely scale GenAI without sacrificing business velocity.
Looking to safely deploy AI agents or set up robust data recovery? Reach out to Wise IT — an official Veeam partner and leading AI solution provider! Our experts offer free consultations on the best IT practices for your specific industry: