The Bug Bounty model has risen to the testing challenges posed by artificial intelligence – as evidenced by the major AI labs’ own heavy investment in crowdsourced security.
Because AI systems interpret natural-language instructions, produce probabilistic outputs and operate with delegated authority, they demand a distinct approach to testing, programme scoping, researcher selection and findings assessment.
YesWeHack has launched and optimised dozens of Bug Bounty programs covering AI systems, from simple chatbots to multi-agent architectures with tool-calling capabilities – developing dedicated frameworks, rule templates, reward models, triage methods and researcher-mobilisation strategies along the way.
Human expertise remains essential. AI tools can accelerate security research, but still struggle to validate exploitability, especially in other non-deterministic systems. YesWeHack’s experts-in-the-loop approach combines AI-assisted testing by diversely skilled researchers with an experienced triage team to validate findings and assess real-world impact effectively.
The interplay between AI systems and the conventional architecture beneath them generates classic vulnerabilities (XSS, IDOR), AI-specific ones (prompt injection, system-prompt leakage) and model-behaviour issues (hallucinations, jailbreaks).
The second in a three-part series, this article focuses on testing the integration layer: the source of AI-specific vulnerabilities.
The three pillars of AI security testing
Depending on the customer’s threat model, integration maturity and testing priorities, AI-powered systems are tested through a bespoke combination of three distinct but complementary approaches:
- Testing the foundation: securing the stack around AI
- Testing the integration layer: AI-specific vulnerabilities
- Testing the guardrails: model behaviour and misuse resistance
Program structure, scoping rules, researcher profiles and reward policies are tailored accordingly.
Testing the integration layer: AI-specific vulnerabilities
What it covers → Risks arising from AI architectures, integration patterns and the ways models interact with connected systems. The impacts often resemble those of traditional vulnerabilities – such as data exfiltration, access to secrets, unauthorised actions, privilege escalation and lateral movement – and can be equally severe.
What distinguishes these vulnerabilities is the attack path: they exploit how a model interprets untrusted input, accesses data, invokes tools or influences connected services.
The most common vector is direct or indirect prompt injection. Others include training-data extraction, model inversion, system-prompt leakage, model-mediated data exfiltration, tool-calling abuse in agentic architectures, MCP server exploitation, RAG (Retrieval-Augmented Generation) poisoning, insecure function-calling chains, privilege escalation through agentic tool use, and cross-plugin or cross-tool attacks in multi-agent systems.
Why it matters → AI systems interpret natural language instructions, operate with delegated authority, and often have access to tools, data sources and actions far exceeding what a traditional user interface would expose. An agent with MCP tool access can read emails, query databases, create files or trigger business processes.
If an attacker hijacks that behaviour through a prompt crafted in a seemingly innocent document, the blast radius can be huge, potentially resulting in data exfiltration, unauthorised actions performed with the agent's privileges and lateral movement across connected services.
Chained exploitation is where this becomes critical. A prompt injection alone may look low-impact in isolation, but when the compromised model has tool-calling capabilities – reading from a CRM, writing to a ticketing system, browsing the web, executing code – it becomes the entry point for a full attack chain with critical business impact. We've observed and validated such chains across multiple programs.
How we test it → These programs can layer on top of existing Bug Bounty scopes or run as focused engagements. They require more deliberate scoping: rules must clearly define which AI-specific attack scenarios are in scope, what counts as a qualifying vulnerability versus a known limitation, and how impact is assessed for findings that don’t fit neatly into traditional CVSS-based frameworks. Reward policies may need adaptation – a prompt injection that achieves data exfiltration through an agentic tool chain warrants a fundamentally different severity assessment than one that merely produces an off-topic response.
Researcher selection matters even more. We mobilise hunters with demonstrated expertise in LLM exploitation, agentic security research and AI red-teaming, alongside strong application security skills.
Triage must be equally equipped: distinguishing a cosmetic guardrail bypass from a structurally exploitable injection path takes specific competence our analysts have built through years of handling these exact submission types.
AI vulnerabilities from YesWeHack AI Bug Bounty Programs
- Indirect prompt injection through poisoned documents ingested by a RAG pipeline, causing the model to expose other users’ data
- Tool-calling abuse that induced an agentic assistant to invoke authorised internal APIs that the user should not have been able to trigger
- System-prompt extraction exposing internal business logic, API keys and access credentials
- MCP server exploitation, where a malicious or compromised tool server injected instructions into the agent’s context and hijacked subsequent tool calls
- Cross-context data leakage caused by improper session isolation, exposing data from previous users’ conversations
- A critical attack chain combining indirect prompt injection, agent tool invocation, SSRF through an HTTP-enabled tool and internal network reconnaissance
What YesWeHack delivers for AI security testing
Program design expertise. We have built and operated dozens of AI-focused Bug Bounty programs across all three testing categories: the application layer around AI (discussed above), AI architecture and integration risks and model behavior, guardrails, and misuse resistance. We maintain ready-to-deploy rule templates, vulnerability taxonomies (qualifying and non-qualifying) and reward models specifically calibrated for AI scopes – from conventional web and API testing on AI-powered applications to adversarial model evaluation.
Triage competence. Our triage teams have developed AI-specific expertise through hands-on exposure to real-world findings. They understand the nuances: distinguishing a cosmetic prompt leak from a structurally exploitable injection, assessing the practical impact of a guardrail bypass, evaluating chained attack scenarios involving agent tool use, and contextualising findings within the customer's specific deployment, business logic and threat model.
A proven researcher community. Bug Bounty hunters are early adopters of the latest tools and techniques and AI is no exception. Our researcher community includes specialists across the full attack spectrum, from classic application security to LLM red teaming, agentic exploitation and adversarial ML. We can mobilise the right profiles for any scope, whether the objective is broad coverage or targeted testing of a specific attack surface.
Years of operational experience. This is not theoretical capability. We have been running AI security programs in production for years, across a wide diversity of scopes: text and voice chatbots, AI-powered customer service platforms, enterprise copilots, document analysis systems, code generation tools, recommendation engines, multi-agent orchestration platforms and business applications deeply integrated with models. We have validated findings at every severity level, from informational to critical, across every category of AI-specific risk.
Adaptability. Whatever your AI deployment looks like – a standalone chatbot, an LLM embedded in a business application, a multi-agent architecture with MCP integrations, a fine-tuned model serving a regulated use case – we can tailor the testing model to your specific attack surface, risk appetite and operational constraints. We work with you to identify the relevant risks, define the right scope and boundaries, and continuously optimise the program to deliver actionable results.
See the YesWeHack platform in action
If you’re looking to expand or improve your security testing program, YesWeHack can help.
YesWeHack provides a full range of automated and human-led testing capabilities that can be combined and customised to fit your security and compliance needs.
Contact YesWeHack for a no-obligation live demo and review of your testing needs.
