Your agents will be attacked.
Make the attack powerless.AI agent security that assumes prompt injection, contains it, and proves every action.

AI agent security is the practice of protecting autonomous AI agents — LLMs that can plan, use tools, call APIs and act on your systems — from being manipulated into harmful actions, and proving what they did. It goes beyond model safety: an agent has real permissions, so the risk is an action, not just an answer. RankShield secures agents by architecture — every agent is a verifiable principal with bounded authority, injected instructions are contained, and every action emits a post-quantum-verifiable receipt.

Prompt injection is an attack where adversarial text overrides an AI agent’s instructions — telling it to ignore its rules, leak data, or misuse a tool. It tops the OWASP Top 10 for LLM applications (LLM01). Direct injection comes from a user; indirect injection hides in content the agent reads — a web page, email, document or API response. Because an LLM cannot reliably tell instructions from data, you cannot fully prevent injection. RankShield’s answer is to assume it happens and make a successful injection powerless.

Indirect prompt injection is when malicious instructions are planted in content an agent consumes rather than typed by the user — a hidden line in a web page, a booby-trapped email, a poisoned document or tool output. When the agent reads that content, the instructions can hijack it without the user ever seeing them. It is considered the most dangerous form because agents autonomously fetch untrusted content. Containment — bounding what any action can do and refusing out-of-policy tool calls — matters more than trying to filter every input.

The OWASP Top 10 for LLM Applications (2025) lists the leading risks for AI systems: LLM01 Prompt Injection, LLM02 Sensitive Information Disclosure, LLM03 Supply Chain, LLM04 Data and Model Poisoning, LLM05 Improper Output Handling, LLM06 Excessive Agency, LLM07 System Prompt Leakage, LLM08 Vector and Embedding Weaknesses, LLM09 Misinformation, and LLM10 Unbounded Consumption. OWASP’s Agentic Security Initiative extends this to agent-specific threats like memory poisoning, tool misuse, privilege compromise and identity spoofing. RankShield maps its controls to these categories.

Excessive agency (OWASP LLM06) is when an agent has more capability, permission or autonomy than its task needs — so a single manipulation can cause outsized damage. It is the reason a prompt injection becomes a breach: if the agent can delete records, move money or email anyone, an attacker who hijacks it can too. The fix is least authority: give each agent a narrow, explicit manifest of allowed actions, require confirmation for high-impact ones, and refuse everything else by default. RankShield enforces bounded authority per agent.

You issue the agent a cryptographic identity — a key pair and a verifiable credential that says which agent it is, who owns it and what it may do — so every action can be attributed and authorized. This matters because non-human identities now vastly outnumber human ones, and an unattributable agent is an ungovernable one. RankShield enrolls every agent as a verifiable principal with a post-quantum-capable identity, so its actions are signed, authorized against policy, and traceable to a real owner.

RankShield contains a compromised agent at the action layer, not the prompt layer. Even if an injection succeeds, the runtime checks every intended action against the agent’s bounded manifest and policy: out-of-scope tool calls are refused, high-impact actions require authorization, and outputs are schema-validated. The injected instruction never reaches a tool with real power — it is made powerless — and the attempt is still recorded as a verifiable receipt. This is the doctrine: assume injection, make it powerless, prove it.

You prove it with a verifiable receipt: every agent action is signed and logged so anyone can independently confirm what happened, in what order, and that the record was not altered. RankShield signs each action with composite post-quantum signatures (ML-DSA-65 paired with a classical algorithm) and anchors them in a tamper-evident transparency log. The result is an audit trail you do not have to trust — you can verify it yourself, which is exactly what emerging AI governance and assurance expects.

Multi-agent systems and tool protocols like MCP (Model Context Protocol) widen the attack surface: agents call other agents and connect to external tools, so a compromise or a poisoned tool can cascade. The controls are the same, applied between agents: each agent and tool authenticates as a verifiable principal, every cross-agent request is authorized against policy and least authority, and every hop is receipted. RankShield treats agent-to-agent and agent-to-tool calls as governed, attestable actions rather than implicit trust.

No — and no honest vendor should claim that. Because an LLM cannot perfectly separate instructions from data, no product can guarantee it will never be injected. RankShield’s claim is different and verifiable: assume injection will sometimes succeed, and make it powerless by bounding authority and refusing out-of-policy actions, then prove every action with a receipt you can check. Security by architecture and verifiability, not an unfalsifiable promise of prevention.