REVIEW 3 cited by
Security Threats in Agentic AI System
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
This research paper explores the privacy and security threats posed to an Agentic AI system with direct access to database systems. Such access introduces significant risks, including unauthorized retrieval of sensitive information, potential exploitation of system vulnerabilities, and misuse of personal or confidential data. The complexity of AI systems combined with their ability to process and analyze large volumes of data increases the chances of data leaks or breaches, which could occur unintentionally or through adversarial manipulation. Furthermore, as AI agents evolve with greater autonomy, their capacity to bypass or exploit security measures becomes a growing concern, heightening the need to address these critical vulnerabilities in agentic systems.
Forward citations
Cited by 3 Pith papers
-
Feedback-Normalized Developer Memory for Reinforcement-Learning Coding Agents: A Safety-Gated MCP Architecture
RL Developer Memory is a feedback-normalized, safety-gated memory architecture for RL coding agents that logs contextual decisions and applies conservative off-policy gates to maintain 80% decision accuracy and full h...
-
Why Trust Your Agent? Empirical Security Gains from TRiSM-Guided Agentic Workflows in Healthcare
TRiSM-guided agentic workflows reduced RAG poisoning attack success from 31% to 10%, data-field injection from 42% to 25%, eliminated network injection, and raised report accuracy from 72.5% to 86.5% across five LLMs ...
-
Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System
An LLM network-monitoring agent experienced nearly doubled telemetry delays under replayed DoS traffic, and edited memory files led it to choose longer, heavier packet captures, in a two-case test of the MAESTRO threa...
Discussion (0). Sign in to comment.