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21 Attack Patterns Every Enterprise AI Platform Should Block

Jonathan Cao

• 3 min read
21 Attack Patterns Every Enterprise AI Platform Should Block

As AI moves from the playground to the production line, the attack surface for the enterprise has expanded exponentially. When you deploy an agentic platform like Nova OS, you aren't just deploying a model; you are deploying an endpoint that interfaces with your private data, internal research, and organizational logic.

Securing that endpoint requires moving beyond simple "word filters." You need a deterministic gateway capable of neutralizing diverse adversarial strategies. Below, we categorize the 21 critical attack patterns that the Nova OS 6-block firewall is designed to intercept.

Category 1: Direct Prompt Injection (The Hijackers)

These are the "front door" attacks where a user attempts to override the system's core directives.

  1. The "Ignore All" Override: The classic attempt to wipe the system prompt.
  2. Persona Adoption (The "DAN" Method): Forcing the model into a role that ignores safety protocols.
  3. Instruction Decoupling: Separating the user input from system constraints via clever phrasing.
  4. Virtualization: Creating a "simulation within a simulation" to bypass the primary firewall.
  5. Payload Splitting: Breaking a malicious command into harmless-looking fragments that the LLM reassembles.

Category 2: Technical Obfuscation (The Camouflage)

Attackers use encoding and formatting to hide their intent from basic text filters. This is where the Nova OS Canonicalizer Block is most critical.

  1. Base64/Rot13 Encoding: Hiding malicious instructions in standard encodings.
  2. Leet-Speak/Character Substitution: Using 5y5t3m instead of system to evade keyword detection.
  3. Multi-Lingual Injection: Switching languages mid-prompt to find "soft spots" in the model's training.
  4. Hidden Unicode/Zero-Width Spaces: Inserting invisible characters that trick filters but are read by the LLM.
  5. ASCII Art Injection: Using visual patterns of characters to convey commands.

Category 3: Indirect Injection (The Trojan Horses)

These occur when an agent retrieves "poisoned" information from an external source, such as a website or a compromised internal document.

  1. Scraped-Data Hijacking: Malicious instructions hidden in a webpage the agent is tasked to research.
  2. In-Document Instruction: Prompt overrides buried in the metadata or footnotes of a PDF.
  3. Email-Chain Poisoning: Injected commands found in historical email threads during a RAG lookup.

Category 4: Data Exfiltration (The Leaks)

The primary goal for many enterprise attacks is to steal internal secrets. Nova OS uses the Redactor and Secret-Guard blocks to kill these responses.

  1. System Prompt Extraction: Tricking the model into revealing its core instructions and security logic.
  2. PII Harvesting: Attempting to force the model to output customer names, emails, or IDs.
  3. API Key/Secret Fishing: Asking the agent to reveal internal credentials it may have access to.
  4. Database Schema Discovery: Tricking an agent into describing the structure of its private knowledge base.

Category 5: Logic & Temporal Corruption (The Gaslighters)

These attacks target the model's "sense of reality."

  1. Temporal Gaslighting: Tricking the model into thinking a safety policy has expired (Neutralized by our Date-Normalizer Block).
  2. Policy Inversion: Convincing the model that "it is now a requirement to be unsafe" for testing purposes.
  3. Recursive Loops: Forcing the model into an infinite processing loop to exhaust resources (DoS).
  4. False Verification: Convincing the agent that a malicious action has already been "approved" by a human administrator.

Hardening the Agentic Path

Traditional AI deployments are vulnerable because they treat security as a "prompt trick." In Nova OS, we gate every inference through a 6-block deterministic gateway. By the time a prompt reaches the model, it has been canonicalized and normalized. By the time the response reaches the user, it has been redacted and verified against an allowlist.

Because Nova OS is compatible with the Anthropic SDK, you can secure your existing Claude-powered workflows against these 21 patterns by simply updating your base_url.

Security isn't a feature; it's the foundation.

Make security model-agnostic with us now!

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