mlsanityData to 14 Oct 2026

API Secrets Should Never Become Tokens in the LLM's Vocabulary: A Threat Analysis of API Credential Handling in LLM Agent Systems and an Empirical Evaluation of a Vault-Mediated Execution Boundary

arXiv 2609.33371 ↗First posted 27 Sep 2026cs.CR cs.AI cs.LGcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Tool-using large language model (LLM) agents turn credential hygiene from a storage problem into an execution-security problem. A key pasted into a prompt, or embedded in a system prompt or tool configuration, crosses from an authentication boundary into a data pipeline, where it may persist in conversation history, logs, memory stores, generated code, and error payloads. Prompt injection and excessive agency then convert passive disclosure into unauthorized action. This paper formalizes the credential-exposure threat chain for agentic systems; synthesizes evidence from a platform secret-store incident, vendor-reported secret-sprawl measurement, and OWASP and NIST guidance; and describes a vault-mediated execution architecture in which the model selects a connector identifier while a trusted request boundary supplies authentication. We evaluate a production implementation, Corvic Security Vault, in two controlled black-box experiments. Across 16 probes spanning seven control domains, every probe met its expected outcome: an authenticated GitHub API request succeeded while the credential stayed absent from process environment values, caller-visible request headers, tested filesystem locations, three third-party echo services, and two unrelated API origins; both cloud instance-metadata endpoints were unreachable. We also report a negative result, a connector whose stored header mapping did not satisfy its provider's authentication contract, showing that centralized custody does not by itself guarantee correct configuration. Vault mediation removes several disclosure paths but is necessary rather than sufficient: least privilege, deterministic action authorization, human approval, telemetry redaction, and rotation remain independently required. The study is purposive and small, a functional security evaluation rather than a certification.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 21 of its 51 references that are in the map. First posted on arXiv 27 Sep 2026.

Builds on

21 of 51 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025A Measurement Study of Model Context Protocol Ecosystem29 Sep
  2. 2025MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Servers19 Aug
  3. 2025Defeating Prompt Injections by Design24 Mar
  4. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar
  5. 2024LLM Agents can Autonomously Hack Websites6 Feb
  6. 2023Identifying the Risks of LM Agents with an LM-Emulated Sandbox25 Sep

Other references · 15

  1. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  2. 2020Extracting Training Data from Large Language Modelscited by 2,189
  3. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  4. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 864
  5. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 832
  6. 2023Prompt Injection attack against LLM-integrated Applicationscited by 605
  7. 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 434
  8. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 431
  9. 2024StruQ: Defending Against Prompt Injection with Structured Queriescited by 375
  10. 2023Poisoning Web-Scale Training Datasets is Practicalcited by 290
  11. 2024Defending Against Indirect Prompt Injection Attacks With Spotlightingcited by 267
  12. 2023AI Control: Improving Safety Despite Intentional Subversioncited by 229
3 more
  1. 2025When MCP Servers Attack: Taxonomy, Feasibility, and Mitigationcited by 17
  2. 2025We Urgently Need Privilege Management in MCP: A Measurement of API Usage in MCP Ecosystemscited by 15
  3. 2016A Comprehensive Formal Security Analysis of OAuth 2.0cited by 4

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
  2. 2024Whispers in the Machine: Confidentiality in Agentic Systems
  3. 2026Securing LLM Agents Need Intent-to-Execution Integrity
  4. 2026CredLeakBench: Evaluating Credential Leakage and Recovery in LLM Agents
  5. 2026You Told Me to Do It: Measuring Instructional Text-induced Private Data Leakage in LLM Agents
  6. 2026How Your Credentials Are Leaked by LLM Agent Skills: An Empirical Study
  7. 2026Delegation Without Trust: An Empirical Gap Analysis of Identity, Authorization, and Runtime Governance in Multi-Agent LLM Systems
  8. 2026Evaluation and Hardening of LLM System Instructions Against Extraction via Encoding Attacks