No industry is immune from the transformative disruption being wrought by artificial intelligence – least of all the Bug Bounty industry. Offensive security practitioners – boundary-pushers by definition – are enjoying the benefits, navigating the challenges and reckoning with the risks sooner than most.
In terms of operational efficiency, AI tools are enhancing decision-making with data-driven intelligence and, through automation, liberating security researchers and security teams alike to focus on areas where they can add the greatest value.
Of course, AI applications are themselves increasingly the subject of security testing. When ChatGPT first wowed the world in 2023 it prefigured a paradigm shift in how applications behave – with profound implications for vulnerability discovery, reproduction and remediation.
In summary, the age of AI likely heralds a supercharging of Bug Bounty fundamentals:
- Ever-more innovative, automated, scalable hacking – for ethical and malicious hackers alike
- Accelerating evolution of attack techniques as AI-specific CWEs continue to emerge
- Novel complexities in validating, reproducing and mitigating AI vulnerabilities
- Streamlining of vulnerability management will reduce time-to-fix, but amid shrinking time-to-exploitation by adversaries
- Increasingly scalable risk mitigation amid accelerating software development and even-faster-growing attack surfaces
This article explores how the intrinsic benefits of Bug Bounty, such as continuous testing, will become even more pronounced (more agile, more scalable) and more necessary as cybercriminals also up their game with the help of AI. Similarly, you’ll learn how the ability to rapidly crowdsource any hacking skill, however niche, is becoming more appealing as AI spawns novel attack techniques amid an acute shortage of cyber skills around emerging technologies.
We’ll also examine the security-testing implications of the spread of AI; what AI cybersecurity tooling means for detecting and handling traditional vulnerabilities like cross-site scripting (XSS), SQL injection and improper access control; and some common AI testing techniques, mitigations and real-world case studies. (And of course, it only seemed appropriate to intersperse the text with AI-generated images!)
#1 The AI explosion in modern applications
AI-powered functionality has become as rapidly integral to modern applications as, for instance, responsive design did before it. Here at YesWeHack, we are already managing several AI-focused programs and dozens of scopes with AI elements.
But the AI rollout is arguably proceeding faster than security practices can keep up with – especially given the technology introduces a set of head-spinning new problems for security testers:
- Probabilistic/non-deterministic behaviour
- ‘Black-box’ problem and emergent (not explicitly programmed) properties
- Daunting scale – potentially billions of parameters and near-limitless range of adversarial scenarios
- Particularly difficult to distinguish intended/benign behaviour from unintended/insecure behaviour
- Complex new failure modes and novel vulnerability types
These AI security concerns make validating, reproducing and mitigating AI-related vulnerabilities particularly complex.
The challenges vary according to the type of system. For instance, machine learning behaviour is at least somewhat inferable. Autonomous agents pose an additional difficulty (autonomous decision-making). A small number of systems, such as spam filters or fraud detection tools, might ‘learn’ in real time – meaning the validity of PoCs might not persist over time.
How has generative AI affected security testing?
The fastest-spreading subtype, gen AI, also happens to be the most challenging to secure. On the testing side, AI systems generally – but especially systems that generate text, images, video and audio – might require:
- Testers with high-level skills, sometimes in niche, complex, relatively new areas of security research
- A patient, careful approach, unencumbered by time constraints given that complex exploits might take weeks or months to bear fruit
- Ability to rapidly launch testing programs, test assets continuously, and be agile in adapting targets, testing conditions and testing coverage to align with ever-shifting testing goals
Traditional pentests, where small numbers of testers conduct snapshot-in-time tests within tight parameters, cannot satisfy these demands, at least not alone. Bug Bounty Programs by contrast subject digital assets to continuous testing by diversely talented hackers, with no time constraints. These bug hunters are either handpicked for their skills and track record (in the case of private programs) or (for public programs) number in the tens of thousands.
Bug Bounty Programs can also be continuously optimised, by tweaking variables such as scopes, rewards and invited hunters, to align with AI assets’ unique and fast-changing security needs (and the organisation’s budgetary constraints). The Bug Bounty platform should provide extensive support in this endeavour, as well as with validating and prioritising (ie, triaging) vulnerabilities.
#2 XSS is here to stay: testing AI scopes for classic vulns and hybrid exploits
While AI has spawned novel CWEs such as data poisoning, model inversion attacks and adversarial machine learning, classic vulnerabilities remain as relevant as ever.
Indeed, pre-AI vulnerabilities – such as this DOM-Based XSS in Elementor AI or the unsecured DeepSeek databaseor OpenAI leak that both exposed chat histories – still account for the majority of validated vulnerabilities associated with AI assets.
The attack surface of AI applications after all still comprises conventional assets such as servers, databases, login pages, APIs and caching mechanisms.
The complex interplay between AI and non-AI components means an insecure AI feature can compromise the wider web application, and vice versa. Consider, for instance, this SQLi/prompt injection vulnerability in AI component GraphCypherQAChain, which processes natural language queries for LangChain, the framework for integrating large language models (LLMs) into applications. Conversely, imagine a broken authentication mechanism that allows unauthenticated users to access internal API endpoints. If a chatbot interacts with users via one such endpoint, that could leave the chatbot open to Denial of Service (DoS) attacks, model poisoning or the malicious triggering of unintended actions.
Will AI take over cybersecurity attack testing?
While traditional testing around, for instance, input validation, access controls or APIs remains as valid as ever, AI tooling is helping hunters find bugs of all kinds more quickly and at greater scale. For instance, a hacker might use AI security tools to deploy multiple instances that test many parts of a target simultaneously. Nevertheless, the most innovative, impactful exploits still require human persistence, creativity and lateral thinking – and indeed, some of the most successful hunters even use tools sparingly.
#3 Hacking AI: Testing for advanced AI and LLM vulnerabilities and risks
As with classic vulnerabilities, ethical hackers typically hunt for AI-specific bugs by emulating the tactics, techniques, and procedures (TTPs) of threat actors. This adversarial testing involves crafting inputs in a bid to induce behaviour that poses legitimate security risks, such as exposing sensitive data about users or the AI model, or enabling attackers to poison or reverse engineer models.
Below are some common types of AI security testing. The applicability of these AI hacking techniques in any given context depends on the nature of the target and the testing goals of the security team:
Prompt injection attack testing
Crafting prompts that induce large language models (LLMs) into running malicious commands, leaking sensitive data, or emitting biased, inaccurate or offensive outputs.
Techniques include: Delimiter confusion, context boundary testing, role-playing, hidden text, nested injection.
Mitigations include: Input sanitisation, context-aware filtering, robust instruction parsing, fine-tuning models to recognise and resist malicious prompts, using human-in-the-loop validation.
Proof of concept: A researcher accessed the system hosting MathGPT, the AI maths tutor, and thereafter a sensitive API key; a student fooled Microsoft’s Bing Chat into revealing its own confidential system instructions (both 2023).
Model extraction attack testing
Repeatedly querying an AI model and using the responses to train a replica model.
Techniques include: Query-based attacks to infer model structure, gradient-based attacks that exploit APIs to approximate gradients, transfer learning or model inversion techniques to extract sensitive information.
Mitigations include: Rate-limiting on API queries, differential privacy (adding ‘noise’ to outputs), returning rounded confidence scores or top-k predictions only, employing model watermarking.
Proof of concept: A researcher extracted internal system prompts from Vercel’s AI chat service, potentially exposing sensitive logic, guardrails or proprietary instructions; researchers found that GPT-3-like models could be partially extracted using API queries (both 2024).
Model poisoning attack testing
Also known as indirect prompt injection, this injects malicious data into the training set in a bid to corrupt a model’s behaviour and embed backdoors.
Techniques include: Poisoning training data with mislabelled or malicious samples, backdoor attacks that embed misbehaviour triggers in data, submitting poisoned updates into federated learning systems.
Mitigations include: Data validation and sanitisation techniques, making training algorithms less sensitive to outliers, monitoring for anomalous patterns in training data. Federated learning systems might use secure aggregation and anomaly detection to filter out malicious updates.
Proof of concept: AI red teamer “pulled off an 11-word 4D jailbreak” of an open-source SOTA model after seeding the internet with custom protocols six months earlier (2025); researchers demonstrated feasibility of poisoning extremely large models with access to only a very small part of their training data (2024).
Privacy leakage testing
Inducing models into inadvertently leaking sensitive information, whether users’ personal data or training data.
Techniques include: Differential privacy testing plus model inversion, membership inference, attribute inference and gradient leakage attacks.
Mitigations include: Differential privacy, decentralising data with federated learning, data anonymisation, stronger access control mechanisms.
Proof of concept: Researchers created an ‘Imprompter’ algorithm that generated ostensibly nonsensical prompts instructing LLMs to find personal information entered by users and then relay them to threat actors.
Membership inference attack testing
Evaluating whether specific data points were part of a machine learning model’s training dataset in order to identify risks of sensitive-data exposure.
Techniques include: Attacks based on thresholds, prediction confidence gaps, shadow models, Bayesian membership inference and entropy.
Mitigations include: Differential privacy (adding ‘noise’ to training process), regularisation methods to reduce overfitting, data anonymisation and access control mechanisms.
Proof of concept: Researchers documented (PDF) a membership inference attack against Google's Cloud Vision API, where confidence scores revealed if specific images were part of the model’s training dataset (2020).
Hallucination and inconsistency testing
Eliciting incorrect, nonsensical or contradictory outputs – especially egregious where trustworthiness is paramount, such as for applications offering legal advice or diagnosing medical conditions.
Techniquesinclude: Fact-checking, contradictory and self-consistency prompts; retrieval augmented testing; prompt perturbation; negation testing; and chain-of-thought or time-based logical consistency testing.
Mitigations include: Fine-tuning with high-quality datasets, fact-checking mechanisms, human-in-the-loop validation, reinforcement learning with human feedback (RLHF).
Proof of concept: Researchers induced LLMs into hallucinating URLs, code libraries and dubious CVE fixes. Technique could potentially spread malicious packages into developer environments (2023).
Overconfidence testing
Assessing whether decision-making models’ confidence scores align with actual accuracy. Overconfident models could lead to poor human or AI decision-making (such as for autonomous driving or financial forecasting).
Techniques include: Brier score analysis, gradient-based attacks, softmax entropy reduction attacks, Monte Carlo dropout testing, out-of-distribution testing.
Mitigations include: Temperature scaling, Bayesian methods, ensemble models, regular evaluation of diverse datasets.
Proof of concept: Tesla's Autopilot system was involved in multiple accidents allegedly due to ‘overconfident’ misclassification of road scenarios (2024).
Bias and fairness testing
Testing for discriminatory or unfair AI outputs, especially where models disproportionately disfavour specific groups based on attributes like race, gender or socioeconomic status.
Techniques include: Dataset bias, algorithmic fairness metrics, counterfactual fairness, bias detection and model explainability testing.
Mitigations include: ‘Reweighting’ influence of certain datapoints, making datasets more diverse and representative, implementing fairness-aware algorithms.
Proof of concept: The COMPAS tool, used by US courts to predict likelihood of prisoners reoffending, exhibited racial bias by disproportionately labelling Black defendants as posing a high risk of recidivism.
#4 Managing AI Bug Bounty Programs
AI does not alter the basics of Bug Bounty management – in fact, the fundamentals become only more fundamental.
For instance, it’s even more imperative to continually align program parameters – scopes, rewards, testing conditions, participating hunters – with the testing needs of a technology that evolves rapidly and unpredictably. And with increasingly complex attack surfaces expanding and in-the-wild exploitation accelerating, the same applies to risk-based prioritisation, time-based SLAs, and leveraging remediation feedback loops to optimise testing conditions and make application development more secure.
These goals require productive input from not only security teams but the Bug Bounty platform’s customer success and triage support teams (increasingly aided, of course, by the judicious use of AI tools).
Setting scopes and testing conditions
AI scopes typically reference the same classic qualifying vulnerabilities as non-AI scopes – such as authentication, server-side request forgery (SSRF) and improper access control issues – as well as familiar out-of-scope techniques like denial of service or social engineering attacks.
But the dynamic nature of AI means novel vulnerability categories will continue to quickly emerge and evolve, making it more important than ever to regularly review and update testing guidelines and qualifying vulnerabilities.
A ‘vulnerability’ is not a vulnerability unless a legitimate security impact has been demonstrated, and AI scopes are usually no exception. Qualifying vulnerabilities might therefore include prompt injection attacks that leak user data or execute malicious code, as well as model extraction issues that facilitate adversarial attacks or compromise intellectual property rights.
On the other hand, hallucination ‘vulnerabilities’ tend to be designated as non-qualifying because they are trivially easy to induce and near impossible to definitively fix, while security impacts are difficult to demonstrate (if a model pretends to run malicious code, you have no exploit). If hallucination testing was permitted, it should be in the context of a private program; via a public program it could trigger an avalanche of spammy ‘vulnerability’ reports.
Biased, inaccurate or offensive outputs per se tend to also be out of scope for similar reasons. This even applies to grave safety issues, such as eliciting bombmaking instructions from an LLM. OpenAI for instance requires that model safety issues are reported via a model behaviour feedback form. Although this is not a hard-and-fast rule, model safety testing requires a skillset that only partially overlaps with that of the archetypal ethical hacker. Nevertheless, the Federation of American Scientists has called for more algorithmic bug bounties for AI safety as well as better legal protections for AI researchers.
Whether AI assets are developed in-house, off-the-shelf components or adapted from third-party models is another significant variable. Fully third-party assets might be ruled out of scope, since only the vendor can mitigate vulnerabilities. Some organisations, however, might accept reports in order to coordinate with the AI vendor and apply any upstream updates. Exploits in third-party components that affect in-house assets might also be accepted as valid. This ambiguity is one among several reasons why having a Vulnerability Disclosure Policy (VDP) with a much broader scope is wise.
Validation and remediation
Outsourcing triage to a proactive, objective triage team with experience of handling AI cybersecurity issues is invaluable. “The approach with AI is different because some vulnerabilities are not ‘technical’ and therefore require a different approach to validating and reproducing PoCs,” explains Adrien Jeanneau, YesWeHack’s VP of security analysis. “It’s not like opening Burp Suite and modifying a few queries.”
The boundary separating AI cybersecurity risks from safety or service quality issues is frequently blurred. Moreover, AI Proofs of Concept (PoC) can be highly theoretical or context-dependent.
The risk of researchers being disgruntled by rejected reports or severity downgrades is perhaps, therefore, heightened. Let’s say, for instance, an exploit leverages carefully crafted noise to trick a fraud detection system into misclassifying transactions. Imagine if this exploit was rejected because the PoC requires full knowledge of the model’s internals and an unrealistic level of control over input data.
Alternatively, PoCs can malfunction because of the system’s randomness, or because an exploit is contingent on the tester’s chat history. Bug Bounty Programs might therefore urge hunters to demonstrate that exploits work reliably under certain conditions, and to provide evidence such as conversation logs, screenshots or screen recordings.
Moreover, if production AI systems learn from user input in real time, it might be wise to provide a sandbox or test-model instance to avoid adversely affecting users or jeopardising reproducibility.
Things don’t get any simpler when it comes to remediation. With regression testing, for instance, how do you ensure a prompt injection ‘fix’ works when the model’s output is probabilistic?
Rewards grid
The comparative immaturity of AI severity frameworks makes pricing AI security vulnerabilities a tricky task. But it’s generally true that AI-related vulnerabilities often attract higher rewards than classic vulnerabilities (this is clearly the case for Microsoft’s Copilot program, for instance), for reasons such as:
- Skills being in relatively short supply for these novel, complex attack vectors, and vulnerabilities potentially being more time-consuming to surface
- Serious potential impacts given: models absorb colossal volumes of data through training and user prompts; reputational harm of misbehaving models; and use of AI assets in critical sectors like healthcare, energy and public sector
- Sometimes AI models are allowed to execute code or access other parts of the system
- Subversion of mobile features like voice assistants or facial recognition unlock can compromise core security
- Organisations with first-party AI assets in scope often have large budgets
Reward structures perhaps evolve more rapidly than for conventional assets, based on fast-evolving threat modelling and rapid rollouts to different use cases. Consider for instance how, in February 2025, Microsoft raised maximum rewards for moderate-severity vulnerabilities from zero to $5,000, and expanded the range of reward-worthy vulnerabilities, amid a rapid rollout of generative AI assistants across its product portfolio.
Among other reasons, Bug Bounty Programs might introduce bounty bonuses or special reward tiers for AI scopes in scenarios such as:
To rapidly harden upcoming or newly released models or AI functions being rolled out for critical use cases
- For the first validated vulnerability reported within a newly defined AI scope
- For vulnerabilities impacting core AI functions
- For vulnerabilities impacting components outside the AI system
- For novel or particularly advanced techniques
- For vulnerabilities that compromise sensitive user data
- Multi-stage exploits – such as model manipulation followed by data extraction – might earn multiple rewards
#5 Conclusion
Probabilistic and often inscrutable, AI systems add considerable complexity and novel challenges to the vulnerability management process.
While we’ve emphasised the continuing – and in fact, increased – importance of conventional Bug Bounty best practices, new norms are also emerging to accommodate the peculiarities of AI.
It’s clear that security teams and their Bug Bounty platform must together think hard about configuring appropriate testing conditions and being realistic about what kinds of ‘bugs’ should be fixed and are practical to fix.
They should also be precise and clear in delineating reward tiers and managing hunters’ expectations, lest they are misled into believing that highly theoretical ‘critical’ risks should necessarily result in top-tier payouts.
Key layer in a multilayered approach
As with conventional assets, securing AI requires a multi-pronged testing approach. Depending on the nature of the asset, that might include mechanisms like adversarial red teaming, data supply chain audits, model explainability audits, robustness and stress testing,threat modelling and model scanning for unsafe code.
Bug Bounty is clearly an important part of the mix. This is attested by the successful, ongoing deployment of crowdsourced testing for AI scopes by Google, Meta, Microsoft, OpenAI and Anthropic, to name a few Silicon Valley giants. YesWeHack and our tens of thousands of eclectically skilled hunters are also being entrusted to harden growing numbers of AI-related assets.
It’s important to recognise where Bug Bounty can truly add value – by testing continuously for AI flaws with legitimate security risks and classic vulnerabilities that affect or interact with AI functions – as well as its potential limitations.
Model safety issues are currently largely addressed outside of Bug Bounty Programs. However, perhaps we’ll see a widening range of qualifying vulnerabilities over time, as ethical hackers expand their skillsets and more model safety testers sign up to Bug Bounty platforms.
Given how AI systems and vulnerabilities interact with traditional architectures and vulnerabilities, Bug Bounty platforms can offer a unique, potent and large-scale blend of relevant skills, deployed continuously, to organisations with AI assets to harden.
Combatting AI risk with AI tools
Of course, hunters and security teams alike will increasingly use AI tools to help them identify and remedy AI vulnerabilities faster than AI-augmented threat actors can exploit them.
YesWeHack has implemented, and continues to devise, AI tools that automate and optimise the vulnerability management process. Among other things, AI is streamlining report-writing, improving the detection of duplicates and false positives, and making data-driven severity predictions.
But this is no excuse to downsize the human dimension – indeed, our triage and customer-success teams are still growing. “It’s important to keep the human brain involved in triaging to ensure the impact reflects the context, our knowledge and the customer’s knowledge,” Adrien Jeanneau, YesWeHack’s VP of security analysis, has said in an interview about our triage service.
Data-driven AI intelligence and automation can enhance decision-making and make vulnerability management more streamlined and less time-consuming.
All this AI-powered automation can translate to:
- More precise, consistent, complete reports – meaning fewer follow-up messages and bottlenecks
- Streamlined reporting and triage processes leading to faster remediation and payouts
- Faster payouts mean happier, more engaged hunters
- More objective, data-driven prioritisation means faster mitigation of the most critical risks
- Greater capability to cope with unexpected manpower shortages or surges in vulnerability reports
Unleash the power of our hunters – get in touch with our sales team or book a demo of our Bug Bounty platform.