Proof pending. Core topic summary fields are still materializing.
Recent advancements in AI security are addressing critical vulnerabilities in generative models and automated systems. One notable trend is the development of latent space watermarking techniques, which enhance the robustness and efficiency of watermarking AI-generated content, potentially mitigating copyright infringement and misuse. Concurrently, tools like HubScan are being introduced to detect hubness poisoning in retrieval-augmented generation systems, a significant security threat that can manipulate content retrieval and filtering. The emergence of frameworks such as Jailbreak Foundry is facilitating reproducible benchmarking of jailbreak techniques for large language models, ensuring that security assessments remain relevant amid rapidly evolving threats. Additionally, innovative approaches like SpecularNet are enabling reference-free phishing detection, improving scalability and practicality in combating web fraud. These efforts reflect a growing recognition of the need for proactive security measures in AI applications, as researchers strive to create more resilient systems capable of withstanding sophisticated attacks.
Topic-specific paper and score movement from the daily diff ledger.
Existing approaches for watermarking AI-generated images often rely on post-hoc methods applied in pixel space, introducing computational overhead and potential visual artifacts. In this work, we expl...
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 j...
Retrieval-Augmented Generation (RAG) systems are essential to contemporary AI applications, allowing large language models to obtain external knowledge via vector similarity search. Nevertheless, thes...
This paper investigates the challenging task of detecting backdoored text-to-image models under black-box settings and introduces a novel detection framework BlackMirror. Existing approaches typically...
Black-box adversarial attacks on Large Vision-Language Models (LVLMs) are challenging due to missing gradients and complex multimodal boundaries. While prior state-of-the-art transfer-based approaches...
Automation platforms such as GitHub Actions and n8n are increasingly adopting so-called agentic workflows, which integrate Large Language Model (LLM) agents for tasks such as code review and data sync...
Secure code review is critical at the pre-commit stage, where vulnerabilities must be caught early under tight latency and limited-context constraints. Existing SAST-based checks are noisy and often m...
Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet remain vulnerable to indirect prompt injection. Adver...
Phishing remains the most pervasive threat to the Web, enabling large-scale credential theft and financial fraud through deceptive webpages. While recent reference-based and generative-AI-driven phish...
OpenClaw's ClawHub marketplace hosts over 13,000 community-contributed agent skills, and between 13% and 26% of them contain security vulnerabilities according to recent audits. Regex scanners miss ob...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID ai-security | Route /topic/ai-security
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/ai-securityMCP example
{
"tool": "search_papers",
"arguments": {
"query": "AI Security",
"cluster": "AI Security"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "AI Security",
"normalized_query": "ai-security",
"route": "/topic/ai-security",
"paper_ref": null,
"topic_slug": "ai-security",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.