Intersections · 14 links
Where the three subjects actually meet
An entry appears here only if it does real work in more than one of the three literatures. Papers that cite another field in passing are not included, which is why this page is short.
What is missing is the point. Deception against machine attackers is now a real, if young, line of work. The link between quantum cognition and either security topic is almost entirely absent. I found one entry applying quantum-cognition machinery to a language model, and it mostly reports an obstacle. I found nothing modelling an autonomous attacker's belief state in quantum-probability terms, and nothing evaluating whether cognitive-bias-based deception transfers to model attackers on the strength of that theory. That may be a gap in the literature or a gap in my searching; About says which parts I am confident about.
The three pairings
Nothing matches those filters. Try clearing one of them.
Attackers + Deception 11
11 entries that belong to both literatures, not just to one that mentions the other.
-
LLM-powered web honeypot: generates a plausible HTTP response to whatever arrives instead of emulating fixed applications. Go, Apache-2.0.
living# -
DEF CON community track on adversary simulation, emulation tactics and purple teaming. The programme page links no recordings at all, so it is hard to follow remotely.
living# -
Low-code deception runtime, Go, GPL-3.0. The interesting part is the MCP bait tooling: decoys aimed at AI agents rather than human intruders. A commercial product sits on top.
-
Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks
Plants adversarial text in responses an attacking agent will read, disrupting it or compromising the attacker's own machine, with over 95% reported effectiveness. The cleanest statement of deception aimed at machines rather than people.
-
Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers
Twenty-one models against 174 reconnaissance queries, finding LLMs take deceptive bait far more often than humans, show no attention-diversion effect, and act on traps 73.4% of the time despite naming them in their reasoning.
preprint# -
Four cooperating agents divert jailbreak attempts into decoy responses, reporting a 68.77% average reduction in attack success while leaving legitimate queries intact. Preprint; the threat model is model-level, not network-level.
preprint# -
Intelligent interactive honeypots: A systematization of AI-driven cyber deception
Systematizes forty studies on AI-driven interactive honeypots, mapping interaction level to attack stage and calling out unstandardised datasets and evaluation as the field's main weaknesses. Carries a 2027 issue date.
-
LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems
Fine-tunes an open-weights model on captured attacker sessions to generate honeypot responses, then evaluates realism and deploys it live. Preprint; evaluation is thinner than shelLM's.
preprint# -
LLM in the Shell: Generative Honeypots
shelLM, an LLM-backed Linux shell honeypot reporting a 0.90 true negative rate against security experts asked to tell it from a real host. The paper that started the generative-honeypot line.
-
The Mantis decoys as running code: tarpitted FTP, deliberately vulnerable web apps, weak telnet, injection payloads, reverse-shell listeners.
living# -
SoK: Honeypots & LLMs, More Than the Sum of Their Parts?
Systematizes both directions at once: LLMs used to build honeypots, and honeypots built for LLM attackers. Includes a taxonomy of honeypot detection vectors and a critique of how the area evaluates itself.
preprint#
Attackers + Quantum cognition 2
2 entries that belong to both literatures, not just to one that mentions the other.
-
Takes the parameter-free QQ equality from quantum cognition and applies it to model log-probabilities, finding most item pairs saturate into near-determinism and so cannot support a distribution-level test. The only direct link between these two literatures found.
preprint# -
Cognitive Bias in High-Stakes Decision-Making with LLMs
BiasBuster, a 16,800-prompt framework for measuring and mitigating cognitive bias in model decisions. Relevant here because deception doctrine assumes exploitable biases; this is the closest thing to an inventory of them in machines.
preprint#
Deception + Quantum cognition 1
1 entry that belong to both literatures, not just to one that mentions the other.
-
A formulation of computational trust based on quantum decision theory
Splits trust into objective and subjective components and uses interference terms to model how evaluations shift between isolated and comparative judgment. One of very few quantum-cognition papers aimed at a security-adjacent problem.
paywalled#