dataset

🚀 CySecBench: Generative AI पर आधारित साइबरसुरक्षा-केंद्रित प्रॉम्प्ट डेटासेट बड़े भाषा मॉडलों के मानकीकरण के लिए 🛡️ बड़े भाषा मॉडलों के मानकीकरण के लिए सबसे बड़ा और सबसे व्यापक Generative AI-आधारित साइबरसुरक्षा-केंद्रित डेटासेट 🌟 अवलोकन CySecBench शोधपत्र प्रस्तुत करता है: 🎯 एक अत्याधुनिक डेटासेट...

ai-llm-red-team-handbook

AI / LLM Red Team फील्ड मैनुअल और सलाहकार हैंडबुक बड़े भाषा मॉडल LLM, AI एजेंट, RAG पाइपलाइन और AI-सक्षम अनुप्रयोगों पर AI/LLM रेड टीम मूल्यांकन करने के लिए एक व्यापक संचालनात्मक टूलकिट। यह रिपॉजिटरी सामरिक क्षेत्र मार्गदर्शन और रणनीतिक परामर्श ढाँचे दोनों प्रदान करती है। 📖 GitBook नेविगेशन: पूर्...

PS4-5.05-Kernel-Exploit

PS4 5.05 कर्नल एक्सप्लॉइट सारांश इस परियोजना में आपको PlayStation 4 के 5.05 पर दूसरे "bpf" कर्नल एक्सप्लॉइट का पूर्ण कार्यान्वयन मिलेगा। यह आपको कर्नल के रूप में मनमाना कोड चलाने की अनुमति देगा, ताकि सिस्टम में जेलब्रेकिंग और कर्नल-स्तरीय संशोधन किए जा सकें। इस एक्सप्लॉइट में Mira और Vortex के HE...

PS4-5.05-Kernel-Exploit

PS4 5.05 कर्नेल एक्सप्लॉइट सारांश इस प्रोजेक्ट में आपको PlayStation 4 पर 5.05 के लिए दूसरे "bpf" कर्नेल एक्सप्लॉइट का पूर्ण कार्यान्वयन मिलेगा। यह आपको कर्नेल के रूप में मनमाना कोड चलाने की अनुमति देगा, ताकि सिस्टम में जेलब्रेकिंग और कर्नेल-स्तरीय संशोधन किए जा सकें। इस एक्सप्लॉइट में Mira और...

Beyond Pass/Fail: Using Process Mining to Understand How LLMs Resist (And Fail) Red Team Attacks

Standard AI red teaming evaluations reduce adversarial campaigns to a single binary outcome, attack success rate ASR, not taking into account the sequential structure of how models resist or yield to attacks. We propose applying process mining, a discipline for discovering and analyzing process...

Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense

Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety classifiers cannot adapt to these evolving attacks, while adaptive memory-based guardrails tend to over-refuse benign queries that resemble stored attacks...

MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models

Diffusion large language models dLLMs generate text by iteratively denoising partially masked sequences under bidirectional context, exposing a safety surface distinct from autoregressive LLMs. Because mask tokens are native inputs and tokens are committed by confidence rather than position,...

Evolving Skill-Structured Attack Memory Enhances LLM Jailbreaking

Jailbreak attacks on large language models LLMs aim to induce LLMs to produce content that they are expected to refuse. Automated black-box jailbreak generation is especially important for safety evaluation, where the attacker observes only model outputs and needs to automatically search for...

Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security

Large Language Models LLMs are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulting in harmful or inappropriate outputs. Such attacks, including jailbreaking and prompt injection, pose significant risks to the integrity and...

Guaranteed Jailbreaking Defense Via Disrupt-And-Rectify Smoothing

This paper proposes a guaranteed defense method for large language models LLMs to safeguard against jailbreaking attacks. Drawing inspiration from the denoised-smoothing approach in the adversarial defense domain, we propose a novel smoothing-based defense method, termed Disrupt-and-Rectify...

Re-Triggering Safeguards within LLMs for Jailbreak Detection

This paper proposes a jailbreaking prompt detection method for large language models LLMs to defend against jailbreak attacks. Although recent LLMs are equipped with built-in safeguards, it remains possible to craft jailbreaking prompts that bypass them. We argue that such jailbreaking prompts ar...

LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments

The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content safety: behavior jailbreak, where an adversary induces an agent to execute dangerous OS-level operations with irreversible consequences. Existing...

Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment

Recent advancements in visual context compression enable MLLMs to process ultra-long contexts efficiently by rendering text into images. However, we identify a critical vulnerability inherent to this paradigm: lowering image resolution inadvertently catalyzes jailbreaking. Our experiments reveal...

Jailbreaking Frontier Foundation Models through Intention Deception

Large vision-language models exhibit remarkable capability but remain highly susceptible to jailbreaking. Existing safety training approaches aim to have the model learn a refusal boundary between safe and unsafe, based on the user's intent. It has been found that this binary training regime ofte...

Jailbreaking the F-35 Fighter Jet

Countries around the world are becoming increasingly concerned about their dependencies on the US. If you've purchase US-made F-35 fighter jets, you are dependent on the US for software maintenance. The Dutch Defense Secretary recently said that he could jailbreak the planes to accept third-party...

Two Frames Matter: A Temporal Attack for Text-To-Video Model Jailbreaking

Recent text-to-video T2V models can synthesize complex videos from lightweight natural language prompts, raising urgent concerns about safety alignment in the event of misuse in the real world. Prior jailbreak attacks typically rewrite unsafe prompts into paraphrases that evade content filters...

Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

Jailbreak techniques for large language models LLMs evolve faster than benchmarks, making robustness estimates stale and difficult to compare across papers due to drift in datasets, harnesses, and judging protocols. We introduce JAILBREAK FOUNDRY JBF, a system that addresses this gap via a...

Recursive Language Models for Jailbreak Detection: A Procedural Defense for Tool-Augmented Agents

Jailbreak prompts are a practical and evolving threat to large language models LLMs, particularly in agentic systems that execute tools over untrusted content. Many attacks exploit long-context hiding, semantic camouflage, and lightweight obfuscations that can evade single-pass guardrails. We...

Jailbreaking Leaves a Trace: Understanding and Detecting Jailbreak Attacks from Internal Representations of Large Language Models

Jailbreaking large language models LLMs has emerged as a critical security challenge with the widespread deployment of conversational AI systems. Adversarial users exploit these models through carefully crafted prompts to elicit restricted or unsafe outputs, a phenomenon commonly referred to as...

TrapSuffix: Proactive Defense against Adversarial Suffixes in Jailbreaking

Suffix-based jailbreak attacks append an adversarial suffix, i.e., a short token sequence, to steer aligned LLMs into unsafe outputs. Since suffixes are free-form text, they admit endlessly many surface forms, making jailbreak mitigation difficult. Most existing defenses depend on passive detecti...