Google’s updated Android and Chrome VRPs signal that AI-assisted bug reports are getting less valuable unless they clearly prove exploitability, impact, and hard-to-automate research.
LiteLLM CVE-2026-42208 shows how a pre-auth SQL injection in an AI gateway can expose provider keys, routing data, and other secrets, turning LLM proxies into high-value credential vaults.
GitHub patched a critical remote code execution flaw in its internal git infrastructure within hours after Wiz Research used AI models to uncover a vulnerability that could expose millions of public and private repositories.
This post explores how malicious prompt injection can be executed via pull request comments in AI-powered coding agents, highlighting security risks and mitigation strategies.
April 2026 showed that AI agents, SaaS integrations, and ordinary web and supply-chain risks now sit in the same AppSec threat model, with untrusted input, OAuth blast radius, and package compromise driving the month's most important security signals.
A practical guide to securing the AI supply chain by validating model integrity and detecting tampering risks.
An overview of how AI-based taint tracking can help detect sensitive data exposure risks across large codebases.
An overview of how AI can enhance fuzz testing for GraphQL APIs to uncover security issues, edge cases, and schema weaknesses more efficiently.
This post explores how LLMs can streamline target enumeration in reconnaissance workflows by automating analysis, prioritization, and data synthesis.
An analysis of the practical balance between AI API usage costs and the security benefits they can deliver.
This post explores how to identify authorization gaps in LLM agent tool use and reduce the risk of unauthorized actions.
Learn how to automate testing for prompt injection vulnerabilities in AI systems using fuzzing tools.
A guide to building a prompt firewall that uses deterministic rules to filter and sanitize user inputs, enhancing security and reliability in AI interactions.
An in-depth exploration of prompt injection attacks, detailing how attackers progress from data exfiltration to achieving remote code execution on vulnerable AI systems.
A practical guide to reviewing AI-assisted code generation for security vulnerabilities and reducing risk in automated development workflows.
A 2026 security incident shows how a compromised AI tool and stolen OAuth access enabled intrusions into internal systems and exposed non-sensitive environment variables, reinforcing the need for MFA, secret rotation, and tighter supply-chain controls.
A practical framework for identifying, evaluating, and mitigating security risks in AI-augmented development pipelines.
A practical checklist for auditing AI-generated code to identify security risks, validate outputs, and strengthen your development workflow.
A practical look at using AI agents for code audit, where they help, where they hallucinate, and why prompt injection and tool scope matter as much as the findings.
A practical look at the common failure modes in SaaS AI integrations, from prompt injection and excessive tool access to weak tenant boundaries and unsafe output handling.
How to test AI-powered web applications for sensitive data exposure, RAG leakage, verbose responses, and unsafe client-side data handling.
A practical look at Model Context Protocol risks, tool descriptions, token exposure, and how JavaScript can help inspect agent tool boundaries.