How malicious content reaches an agent
An agent may read emails, chats, documents, tickets, websites, or uploaded files and then take action. Some of that content is untrusted. Malicious instructions, hidden prompts, misleading context, or poisoned data can manipulate the agent.
Trusted instructions and untrusted content often reach the model through the same token stream. A webpage, message, or document can contain text the model treats as an instruction. The more data and tools the agent can access, the greater the potential impact.
Safety settings are one layer
Safety settings, refusal behavior, prompt engineering, guardrails, and output filtering can reduce some harmful behavior. They are not a reliable security boundary when an agent consumes untrusted content and can act on it.
OWASP lists prompt injection as a leading risk for LLM applications. Treat model-level protections as one layer alongside source restrictions, access controls, tool permissions, approval gates, and logging.
Did Sorena solve AI agent security?
No. Prompt injection remains an open security problem in agentic systems.
Sorena uses a mitigation strategy that reduces exposure and limits what a manipulated agent can reach. The design controls what the agent may read, trust, and use.
Reduce blast radius before you trust the agent
Use several controls to limit prompt-injection impact. Restrict sources, preserve workspace permissions, limit tools by role, require approval for high-risk actions, log retrievals and actions, and require citations for claims that affect a decision.
These controls can keep a manipulated agent from reading outside its workspace, calling tools it was not granted, or approving consequential output without review. Logs and citations also help reviewers trace which source influenced an answer.
The Sorena approach: reduce exposure, ambiguity, and blast radius
Sorena SSOT, our Single Source of Truth, supplies agents with structured, source-linked data rather than unrestricted web content by default.
SSOT centralizes curated regulatory content, standards and frameworks, security datasets, trusted public sources, customer and project documents, and permissioned internal knowledge. That includes laws and regulations, NIST frameworks, ISO and ETSI materials, EU regulatory content, CVE, CWE, and CAPEC security datasets, and project documents uploaded into controlled workspaces. This gives the system clearer trust boundaries around its data.
Why this matters for AI agents
Restricting agents to governed, source-linked, permissioned knowledge can provide:
- Less exposure to hostile instructions
- Less irrelevant or misleading context
- Clearer provenance
- Better traceability and auditability
- More predictable outputs
This does not eliminate risk. Customer documents and other permitted sources may still contain malicious instructions, so the remaining control layers still matter.
Controls for deployed agents
Deployed agents need controls beyond prompt instructions:
- Humans make decisions
- Systems enforce boundaries
- Trusted data is prioritized
- Provenance is visible
- Permissions stay controlled
- Risky actions are constrained
- Outputs stay traceable to sources
Design for hostile instructions
Any agent that reads untrusted content and takes meaningful action needs controls for malicious instructions.
Sorena mitigates that risk through SSOT, governed sources, restricted web dependence, permissions, approvals, and traceable actions. Assume hostile instructions can appear, then limit what they can reach and require review before consequential actions.
Frequently asked questions
Does Sorena claim to have solved prompt injection?+
No. Prompt injection remains a recognized LLM application risk, especially when agents read untrusted content and can take action. Sorena implements a mitigation strategy that materially reduces risk by controlling what agents are allowed to read, trust, and act on.
What is Sorena SSOT and how does it help security?+
SSOT is a governed Single Source of Truth that centralizes curated regulatory content, standards, security datasets (CVE, CWE, CAPEC), trusted public sources, and permissioned internal documents. Feeding agents trusted, source-linked data instead of the open web reduces exposure to hostile instructions, context pollution, and provenance ambiguity.
What controls reduce prompt injection blast radius?+
Start with source restrictions, workspace permissions, role-based tool access, approval gates for high-risk actions, retrieval and action logs, and source citations for decision-impacting claims. OWASP treats prompt injection as a top LLM application risk, so use several controls to limit what a compromised instruction can reach.
