ARXIV:2603.11212 · CODE GENERATION SECURITY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms

arXiv

A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs. However, as practitioners adopt CodeLLMs for increasingly critical development tasks, research reveals that these models frequently generate functionally…

METHOD

Full abstract

Large Language Models (LLMs) show remarkable capabilities in understanding natural language and generating complex code. However, as practitioners adopt CodeLLMs for increasingly critical development tasks, research reveals that these models frequently generate functionally correct yet insecure code, posing significant security risks. While multiple approaches have been proposed to improve security in AI-based code generation, combined benchmarks show these methods remain insufficient for practical use, achieving only limited improvements in both functional correctness and security. This stems from a fundamental gap in understanding the internal mechanisms of code generation and the root causes of security vulnerabilities, forcing researchers to rely on heuristics and empirical observations. In this work, we investigate the internal representation of security concepts in CodeLLMs, revealing that models are often aware of vulnerabilities as they generate insecure code. Through systematic evaluation, we demonstrate that CodeLLMs can distinguish between security subconcepts, enabling a more fine-grained analysis than prior black-box approaches. Leveraging these insights, we propose Secure Concept Steering for CodeLLMs (SCS-Code). During token generation, SCS-Code steers LLMs' internal representations toward secure and functional code output, enabling a lightweight and modular mechanism that can be integrated into existing code models. Our approach achieves superior performance compared to state-of-the-art methods across multiple secure coding benchmarks.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Large Language Models (LLMs) show remarkable capabilities in understanding natural language and generating complex code.

WHY NOW

Code Generation Security moved forward this cycle; last verified April 2026. Public score 8.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score8.0

PainA mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.11212 · CODE GENERATION SECURITY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms

arXiv

A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Large Language Models (LLMs) show remarkable capabilities in understanding natural language and generating complex code.

WHY NOW

Code Generation Security moved forward this cycle; last verified April 2026. Public score 8.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score8.0

PainA mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.11212

Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms

A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materialized8 extracted33 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

8.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Large Language Models (LLMs) show remarkable capabilities in understanding natural language and generating complex code.

WHY NOW

Code Generation Security moved forward this cycle; last verified April 2026. Public score 8.0/10.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
research reveals that these models frequently generate functionally correct yet insecure code, posing significant security risks.
Implicationpartial
Directly and explicitly stated in the abstract as a core problem statement.
Verificationpartial
partial
Evidencepartial
combined benchmarks show these methods remain insufficient for practical use, achieving only limited improvements in both functional correctness and security.
Implicationpartial
Directly stated in the abstract with a clear assessment of current state.
Verificationpartial
partial
Evidencepartial
revealing that models are often aware of vulnerabilities as they generate insecure code.
Implicationpartial
Directly stated as a key finding from investigating internal representations.
Verificationpartial
partial
Evidencepartial
we demonstrate that CodeLLMs can distinguish between security subconcepts, enabling a more fine-grained analysis than prior black-box approaches.
Implicationpartial
Directly stated as a demonstrated result from systematic evaluation.
Verificationpartial
partial
Evidencepartial
During token generation, SCS-Code steers LLMs' internal representations toward secure and functional code output.
Implicationpartial
Directly and explicitly stated as the core mechanism of the proposed method.
Verificationpartial
partial
Evidencepartial
enabling a lightweight and modular mechanism that can be integrated into existing code models.
Implicationpartial
Directly stated as a property of the proposed approach.
Verificationpartial
partial
Evidencepartial
Our approach achieves superior performance compared to state-of-the-art methods across multiple secure coding benchmarks.
Implicationpartial
Directly stated as a performance claim in the abstract.
Verificationpartial
partial
Evidencepartial
This stems from a fundamental gap in understanding the internal mechanisms of code generation and the root causes of security vulnerabilities.
Implicationpartial
Directly stated as the root cause forcing reliance on heuristics.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Code Generation Security

Competitors

not indexed

Competitive landscape

A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.

Segment

Code Generation Security

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.11212 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(33)

Reference metadata pending (a83f93a2e238c2240761918c6adc295d37b33916)

Reference metadata pending (b4ddfb036e01ffc0fc85b7567bedeeab41abeed2)

Reference metadata pending (d9f3b3aca66b35a8d311d72af1731a6941ddefea)

Reference metadata pending (8147e1594d4dfc468300666df149760610b993d6)

Reference metadata pending (c9eba8da86c21df1779f2b1458b9f8e5228b090c)

Reference metadata pending (40e8af970329135ec95057d73e239dab805ad128)

Reference metadata pending (b5efc80da0bd72e7059470c1a9a6c34ab6f966a6)

Reference metadata pending (2797cbda8c845504119b62ee25deb1500ec2dfaf)

Reference metadata pending (087fa49d28506726c337ea3862275462149fc1f9)

Reference metadata pending (66de49b3dcbbf0cca535335d597f94b702e2b95a)

Reference metadata pending (bdf6c0ce7a0a7ea9a724aa802d87e0bedebbbd38)

Reference metadata pending (18e7ab056c16928d8f9539509a4b366889106d97)

Reference metadata pending (3cc4f14f4a174f524115eba6f228409db2856fdf)

Reference metadata pending (12c404768dc2448aac428130a613f5ac5b2a5572)

Reference metadata pending (edd1dc1e8d7989f36c0e54f69f4aeb5e597edc8b)

Reference metadata pending (eb44ce1f7e1f4deac10f6e7009e2073f1eb0b3e4)

Reference metadata pending (db633c6b1c286c0386f0078d8a2e6224e03a6227)

Reference metadata pending (0e0e706e13f160e74cac9556f28ab9a358c148d2)

Reference metadata pending (58fdf550600fc3873729d466601c5d08a51ba8a0)

Reference metadata pending (0b0debb710366cdff461938c80763eace1651af6)

Showing 20 of 33 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkLearn from Your Mistakes: Tree-like Self-Play for Secure Code LLMs

8.0

Extension

Builds On ThisInference-Time Safety For Code LLMs Via Retrieval-Augmented Revision

7.0

Builds On ThisSecurity of LLM-generated Code: A Comparative Analysis

3.0

Builds On ThisDetecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning

7.0

Builds On ThisLLM-Assisted Repository-Level Generation with Structured Spec-Driven Engineering

4.0

Builds On ThisBenchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions

3.0

Builds On ThisDeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation

7.0

Builds On ThisSoK: Robustness in Large Language Models against Jailbreak Attacks

7.0

Builds On ThisWhere Do LLM-based Systems Break? A System-Level Security Framework for Risk Assessment and Treatment

7.0

Builds On ThisToward Reliable, Safe, and Secure LLMs for Scientific Applications

4.0

Commercially relevant

none indexed

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.11212
Route: /paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani
Active tab: read
Artifact: security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani/paper-pack
REST build passport API/api/v1/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "da57af40-1dc0-47c0-a75e-4ee41000a0d0",
  "arxiv_id": "2603.11212",
  "canonical_route": "/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani",
  "endpoints": {
    "paper_pack": "/api/v1/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani/paper-pack",
    "build_passport": "/api/v1/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms

Canonical ID security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani | Route /paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.11212"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms",
  "normalized_query": "2603.11212",
  "route": "/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani",
  "paper_ref": "security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms

/buildability/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani

Watchwatch

Subject: Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani

Paper ref

security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani

arXiv id

2603.11212

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

64c501465fc4d2b5c36fb5d9511690609d91b382fd1c7b5f6e7168b27360a1c7

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani#webpage",
      "url": "https://sciencetostartup.com/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani",
      "name": "Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms",
      "description": "A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani#scholarlyArticle",
      "headline": "Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms",
      "description": "A mechanism to enhance security in LLM-based code generation by steering internal representations towards secure outputs.",
      "url": "https://sciencetostartup.com/paper/security-by-design-for-llm-based-code-generation-leveraging-internal-representations-for-concept-driven-steering-mechani",
      "sameAs": "https://arxiv.org/abs/2603.11212",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2603.11212"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-03-11T18:28:06.000Z",
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a83f93a2e238c2240761918c6adc295d37b33916"
          },
          "url": "https://www.semanticscholar.org/paper/a83f93a2e238c2240761918c6adc295d37b33916"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "b4ddfb036e01ffc0fc85b7567bedeeab41abeed2"
          },
          "url": "https://www.semanticscholar.org/paper/b4ddfb036e01ffc0fc85b7567bedeeab41abeed2"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "d9f3b3aca66b35a8d311d72af1731a6941ddefea"
          },
          "url": "https://www.semanticscholar.org/paper/d9f3b3aca66b35a8d311d72af1731a6941ddefea"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
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            "propertyID": "SemanticScholar",
            "value": "8147e1594d4dfc468300666df149760610b993d6"
          },
          "url": "https://www.semanticscholar.org/paper/8147e1594d4dfc468300666df149760610b993d6"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "c9eba8da86c21df1779f2b1458b9f8e5228b090c"
          },
          "url": "https://www.semanticscholar.org/paper/c9eba8da86c21df1779f2b1458b9f8e5228b090c"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
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            "propertyID": "SemanticScholar",
            "value": "40e8af970329135ec95057d73e239dab805ad128"
          },
          "url": "https://www.semanticscholar.org/paper/40e8af970329135ec95057d73e239dab805ad128"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
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            "propertyID": "SemanticScholar",
            "value": "b5efc80da0bd72e7059470c1a9a6c34ab6f966a6"
          },
          "url": "https://www.semanticscholar.org/paper/b5efc80da0bd72e7059470c1a9a6c34ab6f966a6"
        },
        {
          "@type": "ScholarlyArticle",
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            "propertyID": "SemanticScholar",
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        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "087fa49d28506726c337ea3862275462149fc1f9"
          },
          "url": "https://www.semanticscholar.org/paper/087fa49d28506726c337ea3862275462149fc1f9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
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            "propertyID": "SemanticScholar",
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          },
          "url": "https://www.semanticscholar.org/paper/66de49b3dcbbf0cca535335d597f94b702e2b95a"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
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            "propertyID": "SemanticScholar",
            "value": "bdf6c0ce7a0a7ea9a724aa802d87e0bedebbbd38"
          },
          "url": "https://www.semanticscholar.org/paper/bdf6c0ce7a0a7ea9a724aa802d87e0bedebbbd38"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
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            "propertyID": "SemanticScholar",
            "value": "18e7ab056c16928d8f9539509a4b366889106d97"
          },
          "url": "https://www.semanticscholar.org/paper/18e7ab056c16928d8f9539509a4b366889106d97"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "3cc4f14f4a174f524115eba6f228409db2856fdf"
          },
          "url": "https://www.semanticscholar.org/paper/3cc4f14f4a174f524115eba6f228409db2856fdf"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "12c404768dc2448aac428130a613f5ac5b2a5572"
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          "url": "https://www.semanticscholar.org/paper/12c404768dc2448aac428130a613f5ac5b2a5572"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "edd1dc1e8d7989f36c0e54f69f4aeb5e597edc8b"
          },
          "url": "https://www.semanticscholar.org/paper/edd1dc1e8d7989f36c0e54f69f4aeb5e597edc8b"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "eb44ce1f7e1f4deac10f6e7009e2073f1eb0b3e4"
          },
          "url": "https://www.semanticscholar.org/paper/eb44ce1f7e1f4deac10f6e7009e2073f1eb0b3e4"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
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Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms

Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms

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