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A financially motivated threat actor exploited various commercial generative AI services to compromise over 600 FortiGate devices across more than 55 countries between January 11 and February 18, 2026. The campaign marks a defining demonstration of how AI is lowering the technical entry barrier to offensive cyber operations, enabling a low- to medium-skilled individual or […]

The post Hackers Leveraging Multiple AI Services to Compromise 600+ FortiGate Devices appeared first on Cyber Security News.

Session 13A: JavaScript Security

Authors, Creators & Presenters: Liam Wachter (EPFL), Julian Gremminger (EPFL), Christian Wressnegger (Karlsruhe Institute of Technology (KIT)), Mathias Payer (EPFL), Flavio Toffalini (EPFL)

PAPER
DUMPLING: Fine-Grained Differential JavaScript Engine Fuzzing

Web browsers are ubiquitous and execute untrusted JavaScript (JS) code. JS engines optimize frequently executed code through just-in-time (JIT) compilation. Subtly conflicting assumptions between optimizations frequently result in JS engine vulnerabilities. Attackers can take advantage of such diverging assumptions and use the flexibility of JS to craft exploits that produce a miscalculation, remove bounds checks in JIT compiled code, and ultimately gain arbitrary code execution. Classical fuzzing approaches for JS engines only detect bugs if the engine crashes or a runtime assertion fails. Differential fuzzing can compare interpreted code against optimized JIT compiled code to detect differences in execution. Recent approaches probe the execution states of JS programs through ad-hoc JS functions that read the value of variables at runtime. However, these approaches have limited capabilities to detect diverging executions and inhibit optimizations during JIT compilation, thus leaving JS engines under-tested. We propose DUMPLING, a differential fuzzer that compares the full state of optimized and unoptimized execution for arbitrary JS programs. Instead of instrumenting the JS input, DUMPLING instruments the JS engine itself, enabling deep and precise introspection. These extracted fine-grained execution states, coined as (frame) dumps, are extracted at a high frequency even in the middle of JIT compiled functions. DUMPLING finds eight new bugs in the thoroughly tested V8 engine, where previous differential fuzzing approaches struggled to discover new bugs. We receive $11,000 from Google's Vulnerability Rewards Program for reporting the vulnerabilities found by DUMPLING.

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The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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The post NDSS 2025 -DUMPLING: Fine-Grained Differential JavaScript Engine Fuzzing appeared first on Security Boulevard.

Superagentic AI has released SuperClaw, an open-source, pre-deployment security testing framework built specifically for autonomous AI coding agents. Announced in late 2025, SuperClaw addresses a growing blind spot in enterprise AI adoption: agents are routinely deployed with broad tool access and high privileges, yet most organizations skip structured security validation entirely before going live. The […]

The post SuperClaw – Open-Source Framework to Red-Team AI Agents for Security Testing appeared first on Cyber Security News.