arXiv subjects

Zain Shamsi

Publications and source records attributed to Zain Shamsi.

3 recordsLinked to original sources

arXiv2026

I Think, Therefore I Attack: Evaluating Thought Injection Attacks on Reasoning Agents

Large Reasoning Models (LRMs) are one of the key components of current agentic systems. LRMs augment their output with an explicit chain-of-thought reasoning stage, introducing a new attack surface for thought injection, in which untrusted text resembling the model's own deliberation is fed back to the model. We present a two-part study of this attack, performed against the prevailing large, production class open-weight models (e.g., GLM-5.3, MiniMax-M2.5, Qwen3.8-Flash-Next). We first evaluate the effectiveness of static thought-injection prompts under different styles as well as tagged and untagged conditions and find that the effects of thinking tags and payload styles are all properties of the specific target model. In the second study, we exploit this model-specificity in a containerized agentic cyber range, using indirect injection prompts styled in the form of the target model's own reasoning traces to achieve attack success rates (ASR) up to 50% on operational objectives such as reverse-shell exfiltration and repository backdooring. We also find that the same malicious objective in standard prompt injection user voice never succeeds, and thinking tags suppress the agent's tendency to disclose the injection. Our results show that imitating a reasoning model's own thought style is effective for this style of attack, and that tag stripping is an unreliable defense.

cs.AI↗

arXiv2026

AuthRank: Scoring Source Credibility to Counter Poisoning in CTI LLM Pipelines

Large Language Model (LLM) pipelines have become essential for processing Cyber Threat Intelligence (CTI) to quickly extract updated threat information from open source feeds and reports for real time processing. However, these pipelines are susceptible to poisoning attacks, and previously proposed defenses can fail for CTI contexts as cyber threat information is often completely novel, and attackers can easily mimic the stylistic conventions. We propose that the robustness of LLM pipelines can be improved by applying source credibility algorithms on document corpora, using PageRank as an example. We introduce AuthRank, a credibility score computed from citation networks built using explicit citations, LLM-inferred citations, and claim-level entailment, enhanced with time decay and author credibility. Our experiments on the standardized MS MARCO dataset demonstrate improved accuracy under poisoning, and we show proof-of-concept performance on CTI documents and feeds.

cs.CR↗

arXiv2022

Measuring and Clustering Network Attackers using Medium-Interaction Honeypots

Network honeypots are often used by information security teams to measure the threat landscape in order to secure their networks. With the advancement of honeypot development, today's medium-interaction honeypots provide a way for security teams and researchers to deploy these active defense tools that require little maintenance on a variety of protocols. In this work, we deploy such honeypots on five different protocols on the public Internet and study the intent and sophistication of the attacks we observe. We then use the information gained to develop a clustering approach that identifies correlations in attacker behavior to discover IPs that are highly likely to be controlled by a single operator, illustrating the advantage of using these honeypots for data collection.

cs.CR↗