The Most Valuable Bugs to Hunt in the AI-Assisted Development Era

Bug bounty is highly competitive, and to stay ahead, hunters need to know what to look for instead of testing everything blindly. Modern web apps are built fast with APIs everywhere and AI assisted code that can introduce hidden flaws.

In this article, we will discuss the bugs that are most likely to appear in web apps in 2026. These are the issues that help you stand out from the crowd by focusing on real impact rather than sheer volume.

Bug #1 – Broken Access Control

Introduction:
Broken Access Control happens when an application fails to properly check user permissions on the server side, allowing users to access data or perform actions outside their allowed role. These bugs usually come from missing authorization checks, trusting frontend logic, or complex permission systems that break as the app scales. No advanced exploit is needed here, simple request manipulation is often enough.

Common Types & Examples:

• IDOR (Insecure Direct Object References): Accessing other users’ data by changing object identifiers like userId=123 to userId=124.
• Forced Browsing: Reaching restricted pages or files by guessing URLs such as /admin or /config.json.
• Role-Based Access Issues: Low privilege users performing high privilege actions like deleting records or modifying settings.
• Missing Function Level Access Control: Backend APIs for admin functions are accessible without proper checks.
• Privilege Escalation: Gaining higher permissions than assigned, like accessing admin features while logged in as a normal user.
• Token or Metadata Manipulation: Abusing JWTs, cookies, or hidden fields to bypass authorization checks.

Impact:
The impact is high because these flaws directly lead to unauthorized data exposure, data modification, account takeover, and in some cases full application compromise. Broken Access Control remains the top risk in the OWASP Top 10 2025 because it appears in almost every modern application.

How to Prevent:

• Enforce authorization on every server-side request, never trust the frontend.
• Apply the principle of least privilege and deny access by default.
• Use non-predictable identifiers and test all user flows regularly.

Bug #2 – Web LLM Attacks

Introduction:
As organizations integrate chatbots and AI assistants into customer support, internal IT tools, and content workflows, securing these systems has become critical because LLM-backed applications can access data, APIs, and user information that attackers cannot reach directly. Web LLM attacks exploit this extended access, using crafted inputs to manipulate the model’s behavior, trigger unintended actions, or exfiltrate sensitive information, making these vulnerabilities a high-impact, emerging target for security researchers and bug hunters.

Common Types & Examples:

• Prompt Injection: Attackers insert malicious instructions into user prompts or indirectly into training data to make the model perform actions outside its intended scope.
• Tool and API Exploitation: Malicious prompts cause the LLM to access APIs, databases, or connected systems, potentially executing high-privilege operations.
• Insecure Output Handling: AI-generated content rendered in browsers or downstream systems without sanitization can introduce XSS or CSRF risks.
• Training Data Poisoning and Sensitive Data Leakage: Manipulated training data or careless input of confidential information can lead to hidden instructions, bias, or accidental exposure in outputs.
• Model Denial of Service: Attackers overload or manipulate the LLM to cause crashes, slowdowns, or degraded functionality.

Impact:
The consequences extend beyond technical flaws, as a compromised LLM can expose sensitive customer or corporate information, chain with traditional exploits like SQL injection or XSS, and manipulate outputs to deliver malicious or misleading content. Businesses face financial loss, regulatory issues, and reputational damage, while security teams struggle with a new, opaque attack surface that blends AI behavior with traditional web vulnerabilities.

How to Prevent:

• Validate and sanitize inputs: Filter user prompts, impose character limits, and remove unsafe content before processing.
• Harden output handling: Sanitize all AI-generated responses before rendering or using them in APIs.
• Control LLM capabilities: Limit API access, enforce authentication, and use structured prompt formats to guide safe model behavior.

Bug #3 – Business Logic Flaws

Introduction:
Business logic flaws occur when an application works as intended but the underlying rules are insecure, letting attackers misuse functionality for profit, disruption, or advantage. Unlike technical vulnerabilities such as SQL injection or XSS, these bugs exploit gaps in workflow, assumptions, or validations that automated scanners often miss, giving hunters a high chance to find them manually.

Common Types & Examples:

• Price Manipulation: Changing price parameters in a shopping cart to pay less.
• Negative Quantity: Ordering negative amounts to reduce the total cost.
• Workflow Bypass: Skipping steps in a multi-step process to gain unintended access or benefits.
• Promotional Abuse: Using one-time discount codes multiple times.
• Parameter Tampering: Modifying user IDs, roles, or financial amounts in API requests.
• Race Conditions: Exploiting timing gaps between requests and confirmations to bypass checks.

Impact:
The consequences range from financial loss and fraud to privilege escalation and inventory manipulation. Attackers can exploit these flaws to bypass intended controls, extract sensitive information, or manipulate transactions, causing business disruption, regulatory issues, and reputational damage. Unlike typical bugs, they are highly context-dependent and require deep understanding of workflows, making them invisible to most automated tools but extremely valuable when found.

How to Prevent:

• Map workflows carefully: Understand every step of a process and test for skipped or reversed actions.
• Enforce server-side validation: Never rely on client-side checks for price, stock, or permissions.
• Limit privileges and assumptions: Ensure users cannot access actions, data, or resources beyond their role and document all assumptions clearly.

Bug #4 – Broken Authentication and Sessions

Introduction:
Broken authentication and session issues happen when apps fail to properly verify who a user is or how long they stay logged in, and this gives attackers a clean path to take over accounts without touching complex exploits. These bugs show up everywhere from login pages to APIs and SaaS apps, mostly because developers trust client behavior, reuse weak logic, or mishandle session tokens, and attackers love them because one mistake can unlock full access.

Common Types & Examples:

• Weak credentials: Simple or reused passwords that fall fast to brute force or credential stuffing
• Brute-force gaps: No rate limiting or lockout lets attackers try unlimited login attempts
• Broken password reset: Guessable security questions or reusable reset tokens
• Missing MFA: Accounts rely on passwords only, even for admins
• Session fixation: Session ID does not rotate after login, letting attackers reuse it
• Insecure tokens: Tokens stored in local storage or sent without HTTPS

Impact:
Once authentication breaks, everything behind it collapses because attackers can fully impersonate users, steal private data, perform actions as victims, and escalate privileges if the account has higher access. Even a low-privileged account can expose internal pages and APIs that open the door to deeper attacks, and when admin sessions are compromised, the damage often spreads to databases, payments, and internal systems, making this one of the fastest ways to cause real business loss.

How to Prevent:

• Enforce strong auth: Use MFA, strong passwords, and rate limiting everywhere
• Fix session handling: Rotate tokens on login, set expirations, and invalidate on logout
• Secure tokens: Use HTTPS, HttpOnly, Secure, and SameSite cookie flags

Bug #5 – Software Supply Chain Failures

Introduction:
Software supply chain failures happen when attackers slip malicious code into software through third party libraries, build tools, or update systems instead of attacking the app directly. Modern apps depend heavily on open source packages, CI CD pipelines, and external services, so one weak link can silently affect thousands of downstream systems, as seen with SolarWinds and Log4j.

Common Types & Examples:

• Compromised dependencies: Popular libraries get hijacked or poisoned with malicious updates
• Dependency confusion: Public packages override private internal ones during builds
• Typosquatting: Malicious packages use names close to trusted libraries
• CI CD attacks: Build pipelines are breached to inject backdoors into releases
• Unverified updates: Apps auto pull new versions without integrity checks

Impact:
Supply chain attacks scale fast and quietly because the malicious code ships as trusted software. A single compromised package can lead to data leaks, credential theft, ransomware, or full infrastructure access across many customers at once. Detection is slow, response is costly, and the reputational damage is often worse than a normal breach because users were hit indirectly through trust.

How to Prevent:

• Know your dependencies: Maintain SBOMs and track all direct and transitive packages
• Lock and verify builds: Pin versions, sign artifacts, and secure CI CD access
• Reduce trust by default: Remove unused dependencies and avoid auto updates

Bug #6 – Security Misconfiguration

Introduction:
Security misconfiguration happens when systems, apps, or cloud services are left with unsafe defaults, loose settings, or features that were never needed in the first place. These gaps are quiet but dangerous because attackers do not need fancy exploits, they just walk through what was left open, and this is why most real world breaches still start with simple config mistakes.

Common Types & Examples:

• Default credentials still active on admin panels, databases, or internal tools
• Open cloud storage or dashboards exposed to the internet without auth
• Unused ports, services, or debug pages left enabled after deployment
• Detailed error messages leaking stack traces, paths, or secrets
• Missing or weak security headers that leave apps open to browser attacks

Impact:
Security misconfigurations often lead to full system access, silent data leaks, and long term compromise because attackers blend in with normal traffic. One bad setting can expose user data, internal logic, or cloud resources, and cleanup usually costs far more than fixing the config early.

How to Prevent:

• Disable defaults and remove anything you do not actively need
• Use automated scans and config checks on every deployment
• Lock configs into code and enforce them consistently

Bug #7 – Injection Attacks

Introduction:
Injection attacks happen when an application takes user input and directly passes it into a command, query, or model instruction without proper separation or validation. Instead of data staying as data, it becomes executable logic, allowing attackers to change how the application behaves at runtime and force it to run unintended operations.

Common Types & Examples:

• SQL Injection: Manipulating database queries to dump user data or bypass login checks
• OS Command Injection: Injecting system commands through inputs that touch the operating system
• LDAP and XPath Injection: Abusing directory or XML queries to access restricted data

Impact:
A successful injection attack can expose sensitive data, corrupt databases, or give attackers deep control over systems. In real cases, injection flaws have led to full data breaches, service downtime, ransomware deployment, and long term trust damage because once attackers get a foothold, they rarely stop at one bug.

How to Prevent:

• Validate and sanitize all input strictly
• Use parameterized queries and safe APIs
• Apply least privilege across apps and services

Bug #8 – Cryptographic Failures

Introduction:
Cryptographic failures happen when sensitive data is not protected the right way, either because encryption is missing, weak, or used incorrectly. This includes cases where data travels in plain text, old algorithms are still in use, or keys and secrets are handled carelessly, and attackers do not need to break crypto, they just take advantage of how poorly it is applied.

Common Types & Examples:

• Using weak or broken algorithms like MD5, SHA-1, DES, or outdated TLS versions
• Storing or sending passwords, personal data, or tokens in plain text
• Hardcoding encryption keys, API keys, or secrets in source code or configs
• Poor password storage such as fast hashes or no salt
• Broken randomness like predictable tokens, reused IVs, or non secure random functions

Impact:
Cryptographic failures often lead straight to data breaches, credential leaks, identity theft, reputational damage, and regulatory trouble because once data is exposed, it cannot be taken back. Even if other defenses fail, bad crypto removes the last safety net, turning encrypted data into readable data and breaking user trust in a single incident.

How to Prevent:

• Use modern, standard crypto algorithms and enforce TLS everywhere
• Store secrets and keys securely and rotate them regularly
• Hash passwords with slow, salted algorithms like bcrypt or Argon2

How We Can Help?

In 2026, focusing on smart code practices and proactive monitoring is key to staying ahead of automated attacks. Use jsmon.sh to track application-layer vulnerabilities and supply chain risks in real time.

Secure your business, in real-time!