Quality engineering service
Prompt Injection Testing Services
Expose how untrusted instructions can cross trust boundaries, override intended behaviour, influence tools, or disclose protected context in an AI system.
Service explained
What is prompt-injection testing for AI agents?
Prompt-injection testing examines whether untrusted instructions can override an AI application’s intended behaviour, reveal protected context, misuse connected tools, alter stored state, or influence another user or system. QA-CS tests both direct injection, where a user submits a hostile instruction, and indirect injection, where the instruction is hidden in content the agent reads, such as a webpage, document, email, retrieved record, or tool response. The assessment begins by mapping trust boundaries: system and developer instructions, user roles, retrieval sources, memory, tool permissions, secrets, approval steps, and downstream side effects. We then create realistic attack paths, preserve reproducible evidence, and distinguish a concerning model response from an exploitable business impact. Findings are prioritised by reach, required access, repeatability, data exposure, action severity, and the effectiveness of existing controls. Because no single filter eliminates prompt injection, recommendations use layered controls and human approval for consequential actions. Testing provides bounded evidence, not a guarantee of complete security.
Business value
What this service helps you achieve
Test AI agents and LLM applications against direct, indirect, retrieval, tool-use, and multi-turn prompt-injection attack paths.
Test controls across retrieval, tools, memory, and content
Prioritise remediation by realistic business impact
When to use this service
Recognise the need before risk becomes delay.
Prompt injection is a system risk, not just a malicious sentence. We trace untrusted content through system instructions, retrieved documents, web pages, files, memory, tools, and downstream actions to determine whether an attacker can change behaviour or cross a boundary that should remain protected.
- An LLM application reads external or user-controlled content
- An AI agent can call tools or affect business workflows
- Security teams need evidence for launch or remediation
Our approach
Evidence at every stage.
Prompt injection is a system risk, not just a malicious sentence. We trace untrusted content through system instructions, retrieved documents, web pages, files, memory, tools, and downstream actions to determine whether an attacker can change behaviour or cross a boundary that should remain protected.
- 01Map trusted instructions, untrusted inputs, and privileges
- 02Design direct and indirect attack scenarios
- 03Test controls, tool effects, disclosure, and persistence
- 04Verify remediation and document residual risk
Attack coverage
Follow hostile instructions wherever the agent can encounter them.
An agent may receive instructions from chat, retrieved knowledge, uploaded files, web content, messages, tool outputs, or persistent memory. We test how precedence, formatting, encoding, obfuscation, role-play, multilingual content, and multi-turn setup affect the system. Coverage is prioritised around accessible channels and the actions or data they can reach.
- Direct and multi-turn prompt injection
- Indirect injection through RAG, files, pages, and messages
- System-prompt and sensitive-context disclosure
- Tool misuse, cross-user effects, and persistent-state manipulation
Impact-led findings
Separate surprising output from a security consequence.
A successful jailbreak is not automatically the most important issue. We reproduce the path from attacker-controlled input to a protected asset or consequential action, record the required preconditions, and assess what existing controls interrupt the chain. This gives engineering and security teams a clear basis for remediation priority.
- Affected role, asset, tool, or workflow
- Preconditions and attacker capability
- Repeatability and downstream side effects
- Control gaps, evidence, and verification steps
Defence in depth
Reduce exposure with controls that match the architecture.
Prompt instructions alone cannot create a reliable security boundary. Recommendations may include content isolation, least-privilege tool access, typed interfaces, output validation, retrieval controls, data minimisation, approval gates, monitoring, and safe failure behaviour. We retest agreed changes against the original path and nearby variants to confirm what improved and what risk remains.
- Least privilege and scoped credentials
- Validated tool inputs and outputs
- Human approval for consequential actions
- Monitoring, incident signals, and regression tests
Deliverables
Clear outputs your team can use
Documentation is concise, traceable, and written for engineering, product, and business stakeholders.
- Threat model and attack-path inventory
- Reproducible prompt-injection test cases
- Evidence-ranked findings and impact analysis
- Control recommendations and verification report
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What teams usually ask
Reviewed by the QA-CS quality engineering teamContent updated . Scope and controls are confirmed for each engagement.
What is the difference between direct and indirect prompt injection?
Direct injection is supplied by the interacting user. Indirect injection is embedded in content the system reads, such as a retrieved document, webpage, message, file, database record, or tool response.
Can prompt injection be completely prevented?
No single control guarantees prevention. Risk is reduced through layered architecture, least privilege, validation, isolation, monitoring, and human approval for consequential actions.
Will testing expose production data?
The assessment scope, environments, data, credentials, and approval points are agreed before execution. Safer test environments and synthetic or minimised data are preferred wherever practical.
How are findings prioritised?
We consider required access, reach, repeatability, protected assets, data exposure, tool side effects, affected users, existing controls, and realistic business consequences.
Start with clarity
Discuss prompt injection testing services
Share the product, release, or operational challenge. We will help define the right next step.