Access Control
6 resourcesDefenses & Mitigations
RBAC/ABAC for LLMs, credential scoping, and authorization
MetaBackdoor: Exploiting Positional Encoding as a Backdoor Attack Surface in LLMs
Rui Wen, Mark Russinovich, Andrew Paverd + 2 more
Backdoor attacks pose a serious security threat to large language models (LLMs), which are increasingly deployed as general-purpose assistants in safety- and privacy-critical applications. Existing LLM backdoors rely primarily on content-based triggers, requiring explicit modification of the input text. In this work, we show that this assumption is unnecessary and limiting. We introduce MetaBackdoor, a new class of backdoor attacks that exploits positional information as the trigger, without mod
SecureMCP: A Policy-Enforced LLM Data Access Framework for AIoT Systems via Model Context Protocol
Wonbae Kim, Hee-Kyong Yoo
The deployment of Large Language Model (LLM)-generated SQL queries in Artificial Intelligence of Things (AIoT) systems introduces critical security risks, as prompt injection attacks can manipulate LLMs into producing unauthorized queries that expose sensitive data or execute destructive operations. Existing NL2SQL research focuses on query accuracy, while MCP server implementations provide only SQL-level protections without fine-grained role-based access control. This paper proposes SecureMCP,
A Survey on the Security of Long-Term Memory in LLM Agents: Toward Mnemonic Sovereignty
Zehao Lin, Chunyu Li, Kai Chen
Research on large language model (LLM) security is shifting from "will the model leak training data" to a more consequential question: can an agent with persistent, long-term memory be continuously shaped, cross-session poisoned, accessed without authorization, and propagated across shared organizational state? Recent surveys cover memory architectures and agent mechanisms, but fewer center the epistemic and governance properties of persistent, writable memory as the reason memory is an independ
Authorization Propagation in Multi-Agent AI Systems: Identity Governance as Infrastructure
Krti Tallam
The security discussion around agentic AI focuses heavily on prompt injection. This paper argues that multi-agent systems also create a distinct authorization problem: maintaining authorization invariants as non-human principals retrieve data, delegate tasks, and synthesize results across changing boundaries. We call this problem authorization propagation. It is not reducible to prompt injection and is not fully addressed by classical access-control models such as RBAC, ABAC, or ReBAC. The paper
Model Context Protocol (MCP): Security Considerations and Best Practices
Anthropic — Anthropic Documentation
Documentation and analysis of security considerations for the Model Context Protocol, covering authentication, authorization, and tool sandboxing.
Model Context Protocol (MCP): Specification
Anthropic — Anthropic / GitHub
Open protocol specification for connecting AI models to external data sources and tools, enabling standardized tool use with security considerations.