25 articles in this topic.
This topic page curates research-focused writing on AI Safety, with an emphasis on practical security implications, reproducible observations, and implementation-aware takeaways. Instead of isolated summaries, the collection is organized to help you connect attack techniques, defensive controls, and evaluation criteria across multiple papers and project write-ups.
Across 25 articles, this cluster highlights how AI Safety appears in real workflows and where teams commonly miss risk boundaries. The coverage includes paper review, news digest, trend report, research paper, project, tutorial and connects this theme with adjacent areas such as LLM Security, Adversarial ML, Agent Security, so you can move from conceptual understanding to deployable engineering decisions.
This page is maintained as a high-signal index for AI Safety. Use it to follow newer articles first, then branch into adjacent topics and defensive patterns that repeatedly appear across projects and paper reviews.
An analysis of 'shallow safety alignment'—the finding that current LLM alignment concentrates almost entirely on the first few output tokens—and two lightweight interventions (deep safety-recovery augmentation and a token-aware constrained fine-tuning objective) that push safety deeper into the response.
This digest covers major advancements in AI safety, including OpenAI's biodefense efforts and Arm's defensive automation. It also details new research on memory poisoning and prompt fragility in LLMs.
The security boundary of generative AI has definitively shifted from stateless prompt-engineering vulnerabilities to structural and temporal exploits within multi-agent orchestration architectures. Th
The unifying theme of this week's AI security landscape is the critical transition from superficial, syntax-level filtering to deep, state-aware behavioral defenses across both agentic workflows and s
The dominant security theme today is the structural breakdown of boundaries between reasoning engines and executive environments, transitioning the primary threat vector from semantic prompt manipulat
The systematic scaling of automated, AI-driven vulnerability discovery has triggered a structural crisis in legacy patch-management frameworks, as evidenced by the 263% surge in CVEs forcing an overha
The dominant theme this week is the decisive transition from isolated 'model-centric' security toward systemic, hardware-software co-designed infrastructure integrity. As enterprise AI deployments sca
The dominant security theme this week is the transition from atomic, single-turn prompt injections to stateful, multi-turn cognitive exploits that manipulate the context-window dynamics of Large Langu
The dominant theme this week is the collapse of static, text-centric alignment barriers as multimodal models and autonomous agents merge to create highly dynamic execution-level security risks. As dem
Today’s intelligence briefing highlights a critical inflection point in AI security: the formal invalidation of boundary-based sanitization as systems transition to active, kinetic physical execution.
This paper introduces NeuroStrike, a neuron-level attack framework revealing that safety alignment in LLMs concentrates in fewer than 1% of neurons, enabling both white-box pruning and black-box surrogate-guided jailbreaks with strong cross-model transferability.
The dominant theme in today's landscape is the operational shift toward real-time, inference-stage intervention over destructive weight-modification, manifesting in both AI safety steering and highly
The primary security trajectory this week marks a decisive transition away from localized prompt injection toward systemic, stateful exploitation of autonomous, multi-agent architectures. As artificia
The dominant security paradigm of early 2026 is the rapid transition from static, perimeter-based deep learning defenses to dynamic state-space models and automated prompt-to-signature compilation. Th
The enterprise security landscape is undergoing a critical transition as defensive architectures pivot from token-level static guardrails to countering complex, goal-directed agentic exploits. Emergin
The modern AI threat landscape is undergoing a structural phase shift where security boundaries are migrating away from isolated prompt-engineering patches toward compositional, system-level, and hard
The dominant theme this week is the structural vulnerability of agentic integrations that decouple security policies from real-time execution state, leaving enterprise pipelines highly vulnerable to c
The AI security landscape has reached a critical inflection point, shifting from reactive output filtering to deep-stack defense across intermediate reasoning layers (Chain-of-Thought) and physical ex
The single dominant theme this week is the institutional transition of AI safety from academic red-teaming to formalized, monetized application security frameworks at the semantic layer. As major prov
A systematic analysis of LLM text watermarking techniques, defining eight key properties and seven attack methods, while comparing Zero-bit and Multi-bit approaches for identifying and tracing AI-generated text.
An introduction to Pickleguard, a defense mechanism that detects and prevents malicious pickle payloads through static analysis, opcode inspection, and allowlist-based filtering before deserialization occurs.
A comprehensive overview of LLM red-teaming techniques, covering attack strategies from manual prompt engineering to automated jailbreaking methods like GCG, AutoDAN, PAIR, Crescendo, and GOAT, along with defense mechanisms.
A novel zero-shot machine unlearning method using information theory and curvature analysis, enabling efficient removal of data influence without requiring access to the retain set.
An introduction to machine unlearning in Large Language Models, covering the TOFU benchmark, various unlearning methods (GradDiff, NPO, IdkPO, AltPO), and the challenges of maintaining model utility while forgetting specific knowledge.
An analysis of LINT, a novel attack that bypasses LLM safety alignment by exploiting top-k token access to extract harmful content without prompt engineering, achieving near-perfect attack success rates.