The rapid rollout of AI systems is reaffirming and reshaping the Bug Bounty model.
As organisations embed LLMs, autonomous agents and MCP servers into exposed systems, AI components are increasingly handling sensitive data, making decisions and interacting with backend infrastructure.
Yet the underlying stack – web applications, APIs, cloud infrastructure, authentication layers, data pipelines – still relies on more traditional technologies. This presents a hybrid challenge for security testing: mitigating conventional AppSec risks, emerging AI risks and their complex interactions.
AI tools are of course themselves increasingly valuable for security testing. However, AI-specific vulnerabilities in probabilistic, language-driven systems are often difficult to validate for exploitability within context – precisely the area where AI cyber tools typically fall short. This is why YesWeHack’s ‘experts in the loop’ approach, combining AI-assisted researchers with experienced triage teams, is so valuable. It is telling that even the major AI labs continue to invest heavily in Bug Bounty Programs.
YesWeHack has launched and optimised dozens of Bug Bounty Programs with AI-related scopes – from simple chatbot interfaces to complex, multi-agent architectures with tool-calling capabilities. We’ve developed proven frameworks, rule templates, reward models, triage methodologies and researcher mobilisation strategies specifically for this domain.
The first in a three-part series, this article explains YesWeHack’s approach to traditional AppSec testing: what it involves, why it still matters, where it applies, and how it fits into AI-focused Bug Bounty Programs.
RECOMMENDED Map, test, fix, comply: unveiling our unified approach to offensive security
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 foundation: securing the stack around AI
What it covers → Conventional vulnerabilities in web and mobile applications, APIs and supporting infrastructure. Even when embedding AI components, such as chatbot widgets, recommendation engines and document analysis pipelines, these systems remain susceptible to injection attacks (SQL, XSS, SSRF), broken access control, authentication and session management flaws, insecure API design, misconfigurations, information disclosure, and other familiar vulnerabilities.
Why it matters → Deploying an AI feature doesn’t exempt the surrounding application from security fundamentals. In practice, AI integrations often introduce new API endpoints, data flows, storage mechanisms and third-party dependencies – each expanding the attack surface in familiar ways.
For instance, an LLM-powered chatbot could expose users’ conversation histories because the API serving those records contains an IDOR. Similarly, an admin panel used to manage model configurations, prompts or integrations could be exposed because of missing or weak authentication.
How we test it → These scopes integrate seamlessly with standard Bug Bounty frameworks. They can be added to existing programs or launched as dedicated programs. Rulesets, vulnerability taxonomies and reward grids follow established structures, with targeted adjustments to scope boundaries and exclusions – ensuring researchers focus on critical assets and attack scenarios while preventing out-of-scope submissions on unrelated components.
RECOMMENDED Scaling Bug Bounty triage in the AI era
Classic vulnerabilities from YesWeHack AI Bug Bounty Programs
- IDOR on conversation history endpoints could have exposed other users’ chat sessions, potentially including PII and uploaded documents
- SSRF through document ingestion features (eg a ‘summarize this URL’ function used to reach internal services)
- Broken access control on model administration or prompt management interfaces
- API keys or internal service credentials leaked through verbose error messages or debug endpoints on AI-serving infrastructure
- Cross-tenant data exposure in multi-tenant AI SaaS platforms through manipulated context parameters
- Stored XSS via model output rendered unsanitized in the application frontend
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.
YOU MIGHT ALSO LIKE Cybersecurity in a world of AI-accelerated offence: How YesWeHack implements Anthropic’s prescriptions
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.
RELATED Mitigating AI cybersecurity risks with Bug Bounty Programs: A deep dive
