Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-11T01:54:46.391345Z
Paper Citation Record · LEDGER
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios
As of 4 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2605.07830.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
37 of 37 outbound references displayed
- verified exact13
- verified fuzzy19
- unresolved2
- parse uncertain0
- malformed identifier1
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c24baf1d-d848-4afb-a1cb-d373d01b746f · outbound
This paper cites Claude-opus-4-5overview.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Claude-opus-4-5overview
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.456739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 7d672f50-8c6f-48e3-b0e8-e9d296c8b777 · outbound
This paper cites CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T04:10:57.358908Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ff3dc27c-27d5-43b3-a95c-895286f3c217 · outbound
This paper cites {PentestGPT}: Evaluating and harnessing large language models for automated penetration testing.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios {PentestGPT}: Evaluating and harnessing large language models for automated penetration testing
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.453108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 554ed952-3b6e-4162-9a37-257d1a7a1644 · outbound
This paper cites Cognitive bias in decision-making with llms.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Cognitive bias in decision-making with llms
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.454885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation e8af6451-2ddd-4bdd-bd6d-caf5caa9f20a · outbound
This paper cites LLM Agents can Autonomously Exploit One-day Vulnerabilities.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios LLM Agents can Autonomously Exploit One-day Vulnerabilities
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T04:18:27.783675Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 06ec3b40-e457-4a05-82dc-07063da3a2c9 · outbound
This paper cites Gemini API overview.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Gemini API overview
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.437711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 52e37b0f-0a15-4402-b5b4-1676ae64f8be · outbound
This paper cites The Platonic Representation Hypothesis.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios The Platonic Representation Hypothesis
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T04:10:57.336349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 912e9609-892f-485b-992f-85f40fc531e0 · outbound
This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T04:10:57.311376Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 40a22270-d25e-408a-a1a9-8f86cfe5a789 · outbound
This paper cites Benchmarking cognitive biases in large language models as evaluators.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Benchmarking cognitive biases in large language models as evaluators
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.433105Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 94addfab-558c-4ae8-b016-5c4061fa30cd · outbound
This paper cites Your ai, not your view: The bias of llms in investment analysis.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Your ai, not your view: The bias of llms in investment analysis
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.451229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8b279bce-b980-4861-8ddc-a04628d4bc28 · outbound
This paper cites 1991 , publisher =.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios 1991 , publisher =
Reference 11
- Resolution
- verified exact
- doi, observed 2026-05-11T01:55:51.092292Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation b6a6611d-386e-4a39-b611-59aea4d0c491 · outbound
This paper cites AgentBench: Evaluating LLMs as Agents.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios AgentBench: Evaluating LLMs as Agents
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T11:40:05.312349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 51820cb8-8a26-46d0-a2ea-e4b24bc23b63 · outbound
This paper cites Agentboard: An analytical evaluation board of multi-turn llm agents.Advances in neural information processing systems, 37:74325–74362.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Agentboard: An analytical evaluation board of multi-turn llm agents.Advances in neural information processing systems, 37:74325–74362
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.439962Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 813114a9-d494-4e4f-a393-8377afb91939 · outbound
This paper cites an unresolved cited work.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-05-14T15:11:37.447607Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation fa6049ec-a473-4ce9-97eb-dbfb0d1640bd · outbound
This paper cites Common attack pattern enumeration and classification (capec).
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Common attack pattern enumeration and classification (capec)
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.449404Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 0cc314a1-63bf-4073-88e5-3f9ea27f8732 · outbound
This paper cites Common weakness enumeration (cwe).
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Common weakness enumeration (cwe)
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.443941Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c1974f97-acfd-4144-b1f0-c1d8d3678571 · outbound
This paper cites Introducing GPT-5.2 codex.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Introducing GPT-5.2 codex
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.445888Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c9565065-6f90-4fc3-b215-8a3fb4dd4263 · outbound
This paper cites Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.435194Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation e42b9def-d073-4b0a-b12f-ffbaf3584a4f · outbound
This paper cites OWASP ModSecurity core rule set.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios OWASP ModSecurity core rule set
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.460526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 6f50733e-0347-4ded-bf88-d558dcd3f20a · outbound
This paper cites Owasp web security testing guide.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Owasp web security testing guide
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.441836Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 0bf9ed19-7481-4e77-9b32-13fa65e0a1ac · outbound
This paper cites Owasp top 10 web application security risks.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Owasp top 10 web application security risks
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.467771Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c80d12c3-6a2f-40a8-a7f5-cfb844a17bd0 · outbound
This paper cites Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T01:59:51.558892Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ebb40d95-d6cb-4d47-9c25-9f9388f10d3a · outbound
This paper cites A mathematical theory of communication.The Bell system technical journal, 27(3):379–423.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios A mathematical theory of communication.The Bell system technical journal, 27(3):379–423
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.458726Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation b6bdf221-7c1d-4eea-9a87-4459b060f6d6 · outbound
This paper cites Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T04:10:57.344884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 5aa40288-7dc7-4b5b-a43e-b3c845633b61 · outbound
This paper cites Kimi K2.5: Visual Agentic Intelligence.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Kimi K2.5: Visual Agentic Intelligence
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T04:10:57.367863Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 030889f8-ebfd-4060-9a7a-8961fa38f5e8 · outbound
This paper cites Chain-of-thought prompting elicits reasoning in large language models.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Chain-of-thought prompting elicits reasoning in large language models
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.469753Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 3a6bac01-f8f3-41d7-af44-541159f34fba · outbound
This paper cites We're Different, We're the Same: Creative Homogeneity Across LLMs.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios We're Different, We're the Same: Creative Homogeneity Across LLMs
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T04:10:57.307536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ee4d63a9-b184-4d5a-9314-e4179534dda5 · outbound
This paper cites Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.471539Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8d7a2e79-2f86-4371-9057-6c92e40136e0 · outbound
This paper cites AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T04:10:57.315417Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 45d8378f-c0d4-45af-a01a-e2af6f9cfe95 · outbound
This paper cites Swe-agent: Agent-computer interfaces enable automated software engineering.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Swe-agent: Agent-computer interfaces enable automated software engineering
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.473322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 5a647cc8-1ef6-4ab5-b1e9-ca96afb5560d · outbound
This paper cites React: Synergizing reasoning and acting in language models.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios React: Synergizing reasoning and acting in language models
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T15:11:37.465997Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c0fc0023-9473-4d66-972c-39ff48a52f5c · outbound
This paper cites an unresolved cited work.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Unresolved cited work
Reference 32
- Resolution
- unresolved
- raw_fallback, observed 2026-05-14T15:11:37.462234Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 056324e2-fb99-4104-b553-afcf4b86cac3 · outbound
This paper cites Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models
Reference 33
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T04:10:57.348437Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 9717259c-c734-4fd1-9c50-0df65db93099 · outbound
This paper cites Zhang, Joey Ji, Celeste Menders, Riya Dulepet, T.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios Zhang, Joey Ji, Celeste Menders, Riya Dulepet, T
Reference 34
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T04:10:57.325784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ff88499c-a88c-402d-88cb-6b05ea7f9d60 · outbound
This paper cites WebArena: A Realistic Web Environment for Building Autonomous Agents.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios WebArena: A Realistic Web Environment for Building Autonomous Agents
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T04:10:57.330973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c46f35e0-fd53-4d56-89b6-7eac907f5e8c · outbound
This paper cites CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T04:10:57.379648Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8f587563-b2fc-4a42-b605-df2256179cfc · outbound
This paper cites timestamp.
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios timestamp
Reference 37
- Resolution
- malformed identifier
- raw_fallback, observed 2026-05-14T15:11:37.464170Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Pith citing papers
No inbound Pith citation observations are available.