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Notes on ai security
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Breaking the weakest link to evade vision language models
The authors investigate the vulnerability of Vision Language Models (VLMs) to adversarial attacks, which can disrupt or force the models' interpretation of visual inputs. They propose a gradient-based attack method…
ElasticBack: Stealthy Conditional Backdoor in LLM-Agent Skills via Coupled Trigger-Rule Optimization
The paper presents ElasticBack, a stealthy conditional backdoor that can compromise LLM agents by planting a rule and trigger in skills. The backdoor only activates when both the rule and the trigger appear together…
Stealing Reasoning Traces from Proprietary LLM APIs
Researchers have identified a vulnerability in the way large language model providers handle their models' step-by-step reasoning. This vulnerability allows for the decryption of encrypted reasoning traces from one…
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
Researchers propose a system to store AI agent's private keys in hardware-confined devices, improving key confidentiality and authorization. They replace software-stored keys with ones stored in HSMs, TPMs, or smart…
Toward cryptographically verifiable authorization for autonomous AI agents: A security hypothesis, preliminary formal model, and proof-of-concept implementation
This research paper proposes a new concept called Cryptographically Verifiable Agent Authorization (CVA). The authors aim to create a system where AI agents can be authorized to perform actions with cryptographic…
Toward cryptographically verifiable authorization for autonomous AI agents: A security hypothesis, preliminary formal model, and proof-of-concept implementation
The authors propose a new approach to cryptographically verifiable authorization for autonomous AI agents, which would provide evidence that an agent's request satisfies the applicable policy in a specific execution…
Toward cryptographically verifiable authorization for autonomous AI agents: A security hypothesis, preliminary formal model, and proof-of-concept implementation
The paper proposes a cryptographic method to ensure that an autonomous AI agent's authorization request satisfies the applicable policy in a specific execution context, while preserving confidentiality of private…
Self-State Attacks on Self-Hosted AI Agents: How Far Can OS Defenses Go?
This paper investigates the resilience of operating systems (OS) against a specific type of attack on self-hosted AI agents called self-state attacks. These attacks compromise an agent by corrupting its own state via…
Rethinking Penetration Testing for AI-Enabled Systems: From Resource Compromise to Behavioral Objective Violation
The authors propose a new approach to penetration testing for AI-enabled systems. Instead of focusing on direct compromises of infrastructure, they suggest evaluating if adversaries can manipulate system behavior…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
The study reveals that Language Models (LLMs) fine-tuned for security classification can overlook vulnerabilities introduced during fine-tuning itself. These models may learn specific token-level indicators which…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
The study shows that Language Models (LLMs) fine-tuned for security classification can have vulnerabilities missed during standard evaluation because they learn token-level indicator semantics that work well on their…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
The study shows that Language Models (LLMs) fine-tuned for security classification may overlook vulnerabilities introduced during fine-tuning itself. These models can learn token-level indicator semantics that preserve…
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