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15/08/2026 6:12 pm
TL;DR: AI systems need specialized pentesting because prompt injection, model inversion, and memory poisoning create attack paths that traditional scanners miss, according to Obsidian Security. The real shift is that AI security testing now has to treat model behaviour, data access, and agent action as governable risk, not just application output.
NHIMG editorial — based on content published by Obsidian Security: The Top AI Pentesting Tools for LLMs and Autonomous Agents
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.
Questions worth separating out
Q: What breaks when AI pentesting is not built into the release process?
A: Security teams lose visibility into how model changes affect behaviour, tool use, and data exposure.
Q: Why do AI agents complicate existing IAM and NHI controls?
A: They complicate control design because they can select actions at runtime, call multiple APIs, and move authority across systems without a human session boundary.
Q: How do teams know if AI-assisted pentesting is actually working?
A: Look for higher-quality findings, faster triage, and fewer unresolved false positives, not just more output.
Practitioner guidance
- Test prompt and retrieval abuse paths Create adversarial test cases for prompt injection, malicious retrieval content, and indirect instruction chains before any model touches sensitive datasets or internal workflows.
- Inventory AI-connected privileges Map every model, agent, connector, and API token to the data and actions it can reach, then revoke anything that is not strictly required for the use case.
- Add adversarial testing to MLOps gates Trigger security assessments when models, prompts, retrievers, or tool permissions change, and block release if coverage does not include runtime behaviour.
What's in the full article
Obsidian Security's full blog post covers the operational detail this post intentionally leaves for the source:
- Tool-by-tool comparison of commercial and open-source AI pentesting platforms for LLM and agent assessments
- Implementation considerations for integrating AI security testing into MLOps and release gates
- Examples of vulnerability metrics, coverage metrics, and remediation timing used to judge programme maturity
- Guidance on how AI pentesting findings can be folded into broader SaaS and access governance workflows
👉 Read Obsidian Security's analysis of AI pentesting tools for LLMs and autonomous agents →
AI pentesting tools for LLMs and agents: what do teams need?
Explore further
15/08/2026 6:19 pm
AI pentesting is now a governance function, not just a technical test. The article shows that model robustness, tool abuse, and memory poisoning cannot be left to isolated red teams or point security reviews. In identity terms, the issue is not only whether the model works, but what it is allowed to access and execute. Enterprises should treat AI security testing as part of broader access governance, with clear ownership across IAM, security, and MLOps.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: What accountability exists when an AI agent misuses enterprise access?
A: Accountability sits with the organisation that granted the access, defined the workflow, and accepted the control gaps. Risk, security, and system owners should jointly document who approved the permissions, which controls limit the agent, and what evidence shows those controls were tested before production use.
👉 Read our full editorial: AI pentesting tools expose the new attack surface in LLMs
