memory-poisoning-axis

memory-poisoning-axis Deterministic memory-poisoning / prompt-injection measurement — anchored to CVE-2026-24301 "CoSnitch" Microsoft Copilot Personal, CVSS 3.1 8.8, patched 18 Aug 2026. CoSnitch chained: i automatic prompt execution ?q= + undocumented ?autorun=1, ii data exfiltration via...

When Malicious Instructions Persist: Persistent Memory Poisoning Attack on Harness-Based Agents

Harness design has transformed the development of LLM-based agents by integrating memory, tool use, and runtime control. However, this design also introduces security and privacy risks because malicious instructions from external sources may be written into persistent memory and persist across...

MemSentry: A Framework for Detecting Persistent Memory Poisoning in Agentic AI

Agentic AI systems with persistent memory introduce a distinct attack surface known as memory poisoning, in which adversarially crafted content is stored in long-term memory and subsequently influences future agent behavior. Such attacks can suppress security alerts, facilitate privilege...

CAPTURE: Disentangling Preference Drift from Memory Poisoning in Personalized LLM Agents

Personalized language agents use persistent memory to adapt to users over time, but the same mechanism creates an attack surface. When new information conflicts with stored preferences, an agent must distinguish genuine preference drift from temporary context shifts, ambiguity, or adversarial...

AI Recommendation Poisoning: How "Ask AI" Buttons Silently Alter LLM Memory

A new class of prompt injection is spreading across commercial websites. It requires no malware, no stolen credentials, and no zero-day exploit. It abuses a standard feature built into almost every major AI assistant: pre-filled deep links. We observed production websites embedding hidden prompt...

Adversarial Attacks in Multi-Agent LLM Pipelines: Unveiling Structural Vulnerabilities in Agentic AI Architectures

Multi-agent LLM pipelines orchestrate multiple specialized language model agents into structured workflows where intermediate outputs are passed across agents to solve complex tasks. This design introduces a security gap absent in single-agent settings: once an agent accepts adversarial content, ...

MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair

Memory systems allow agents to retain and reuse information from past interactions, but they can also let malicious content persist. A malicious instruction crafted by an attacker may be stored in long-term memory, recalled much later, and quietly shape a real action. Recent benchmarks increasing...

CVE-2026-64182

The CVE-2026-64182 vulnerability concerns the Linux kernel memory subsystem: memblk_nr_poison_inc() and memblk_nr_poison_sub() look up a memory block via find_memory_block_by_id(), which returns a reference to the memory block device. Both helpers use the returned memory block without dropping th...

Serena: Unauthenticated Flask dashboard on fixed port enables DNS rebinding → memory poisoning → RCE

SummarySerena's built-in web dashboard exposes an unauthenticated Flask API on a fixed, predictable port TCP 24282, hardcoded as 0x5EDA in constants.py. The server has no authentication, no CSRF protection, and no Host header validation. A DNS rebinding attack allows a malicious webpage to reach...

GHSA-37H2-6P4F-MP3Q Serena: Unauthenticated Flask dashboard on fixed port enables DNS rebinding → memory poisoning → RCE

Summary Serena's built-in web dashboard exposes an unauthenticated Flask API on a fixed, predictable port TCP 24282, hardcoded as 0x5EDA in constants.py. The server has no authentication, no CSRF protection, and no Host header validation. A DNS rebinding attack allows a malicious webpage to reach...

CVE-2026-49471 Serena: Unauthenticated Flask dashboard on fixed port enables DNS rebinding → memory poisoning → RCE

Serena is a powerful MCP toolkit for coding that provides semantic retrieval and editing capabilities. Prior to v1.5.2, Serena's built-in web dashboard exposes an unauthenticated Flask API on a fixed, predictable port, with no authentication, no CSRF protection, and no Host header validation. A D...

CVE-2026-49471

Serena exposes an unauthenticated Flask API on a fixed port prior to v1.5.2, with no authentication, CSRF protection, or Host header validation. A DNS rebinding attack can reach this API from any browser and write arbitrary content to the agent’s persistent memory store, which the agent may read ...

CVE-2026-49471 Serena: Unauthenticated Flask dashboard on fixed port enables DNS rebinding → memory poisoning → RCE

Serena is a powerful MCP toolkit for coding that provides semantic retrieval and editing capabilities. Prior to v1.5.2, Serena's built-in web dashboard exposes an unauthenticated Flask API on a fixed, predictable port, with no authentication, no CSRF protection, and no Host header validation. A D...

Forensic Trajectory Signatures for Agent Memory Poisoning Detection

We discover a behavioral invariant in LLM agents under persistent memory poisoning: in architectures where routing information is retrieved through observable memory-tool invocations, successful attacks require calling memoryrecallfact before emailsendemail, a transition that non-exfiltrating...

MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents

External memory has become a core component of modern web agents, enabling long-horizon reasoning through the retrieval of past experiences. However, this paradigm introduces a critical vulnerability: malicious content injected into memory can be persistently recalled and repeatedly influence age...

Updating the taxonomy of failure modes in agentic AI systems: What a year of red teaming taught us

In this article 1. Why the Taxonomy Needed Updating 2. Seven new failure modes 3. Operational findings: What red teaming showed 4. New mitigations 5. What to do this quarter When the Microsoft AI Red Team published the Taxonomy of Failure Modes in Agentic AI Systems in April 2025, the goal was a...

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...

From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents

Memory is a core component of AI agents, enabling them to accumulate knowledge across interactions and improve performance. However, persistent memory introduces the risk of memory poisoning, where a single adversarial memory write can exert long-term influence over agent behavior. We present a...

Hijacking Agent Memory: Stealthy Trojan Attacks through Conversational Interaction

Large language model LLM agents increasingly leverage long term memory to support persistent and autonomous task execution. However, this capability also introduces a new attack surface: memory poisoning, where adversaries can inject malicious information to influence future behavior. Existing...

MemMorph: Tool Hijacking in LLM Agents Via Memory Poisoning

LLM-driven agents are capable of selecting external tools to complete users' tasks. However, attackers could compromise such process, steering agents toward inappropriate/wrong tools and enabling malicious actions. Most existing attacks primarily manipulate the tool metadata, which is easily...