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19/08/2026 3:30 pm
TL;DR: LLM applications remain highly exposed to prompt injection, sensitive data disclosure, excessive agency, and output-handling failures, with research cited by Equixly showing 31 successful compromises across 36 tested applications and broader studies finding 45.7% of issues tied to improper resource control. The practical lesson is that LLM security now depends on runtime testing, access control, and auditability, not code-only assurance.
NHIMG editorial — based on content published by Equixly: LLM penetration testing and the state of LLM security
By the numbers:
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes, and as quickly as 9 minutes in some cases.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, or revealing access credentials.
Questions worth separating out
Q: How should security teams handle prompt injection in production LLM applications?
A: Security teams should treat prompt injection as a runtime control issue, not a content-moderation problem.
Q: Why do LLM agents create new identity and privilege risks?
A: LLM agents can call tools, access data, and trigger actions, so their permissions become part of the security boundary.
Q: How do security teams reduce the impact of unsafe LLM output handling?
A: They should block direct execution of model output until it has been validated, normalised, and checked against the intended action.
Practitioner guidance
- Build runtime adversarial testing into CI/CD Run prompt injection and tool-abuse tests continuously in production-like environments, not only during pre-deployment review, so hidden retrieval and tool-call failures are exercised before release.
- Constrain agent permissions to task scope Map every external tool, API, file system, and database access path an LLM can reach, then reduce permissions to the minimum required for each workflow.
- Validate all downstream LLM output Prevent the model from passing raw output into execution layers such as shells, SQL interpreters, or browser automation without validation and sanitisation.
What's in the full article
Equixly's full blog covers the operational detail this post intentionally leaves for the source:
- The article’s step-by-step breakdown of the OWASP LLM attack categories and how each one maps to runtime failures.
- The testing approach used to probe production software with HOUYI and document reproducible evidence.
- The specific remediation guidance Equixly associates with prompt leakage, tool-call abuse, and unsafe output handling.
- The compliance context for EU AI Act, NIS2, and PCI DSS evidence expectations.
👉 Read Equixly's analysis of LLM security risks, prompt injection, and agentic abuse →
LLM security and prompt injection: are your controls keeping up?
Explore further
19/08/2026 4:13 pm
LLM security is now a governance problem, not a model-only problem. The article shows that risk emerges when prompts, retrieval, and tool execution are stitched together into one runtime system. That means the control boundary sits around behaviour, access, and output handling, not around the model artifact alone. Practitioners should govern the full execution path, not just the model.
A question worth separating out:
Q: Who is accountable when an LLM agent acts outside its intended scope?
A: Accountability sits with the organisation that grants the agent access, defines the workflow, and accepts the risk of delegated action. Regulators and auditors will expect evidence of ongoing testing, logging, and access control, especially where the agent can process personal data or influence regulated decisions.
👉 Read our full editorial: LLM security is a runtime governance problem, not a static scan
